Determinants and Interventions for Type 2 Diabetes Type 2 Diabetes - Australia

Type 2 Diabetes in Australia: Determinants and Interventions

Type 2 diabetes by far the most common form of diabetes and is defined by glucose intolerance and elevated blood glucose levels (hyperglycaemia). Other physiological disorders frequently accompany diabetes, including dysregulation of lipid metabolism (dyslipidaemia), kidney function, and cardiovascular disease, which contribute significantly to the morbidity and mortality suffered by diabetes patients (Barr, 2006, p. 6). Unfortunately, the cause of type 2 diabetes in most cases is not known (Barr, 2006, p. iii), but a number of risk factors have been identified that if modified can help improve outcomes.

A Significant Health Issue

Type 2 diabetes mellitus is the fastest growing chronic disease in Australia, with approximately 275 adults developing the disease each day (Barr et al., 2006, p. xi). At this rate 0.8% of the population develops diabetes each year (Barr et al., 2006, p. 7). Current estimates suggest 1.7 million Australians have diabetes (Diabetes Australia -- Vic), which puts the overall prevalence rate near 8%. With the sixth highest mortality rate and an estimated cost to the healthcare system near 4 billion in 2005 (Cameron et al., 2006), considerable investment in diabetes research and interventions is being made.

The risk factors for diabetes include being overweight (2-fold) or obese (4-fold), lack of physical activity (2-fold), hypertension (3-fold), dyslipidaemia (3-fold), and metabolic syndrome (4-fold; Barr, 2006, pp. 9-12). Patients with diabetes were twice as likely to die during a five-year period, a mortality rate similar to smokers and individuals suffering from cardiovascular disease (Barr, 2006, p. 42). A strong interaction between cardiovascular disease and diabetes was revealed by finding that over two-thirds of patients who died of cardiovascular disease also suffered from diabetes.

Obesity

Cameron et al. (2009) investigated the relationship between abdominal obesity and type 2 diabetes within the AusDiab Study cohort. A total of 6072 men and women between the ages of 25 and 75 who had waist circumference data were included in the study. Outcome measures after 5 years were type 2 diabetes, hypertension, dyslipidaemia, metabolic syndrome, and cardiovascular disease, and at 8 years -- mortality. Cameron et al. (2009) found that close to 47.4% and 38.0% of the yearly incidence of diabetes is associated with obesity in women and men, respectively, which translates into 3.3-fold and 3.6 fold increased risk compared to non-obese adults (p. 206).

Diet Quality

A diet rich in sugar and fat, according to conventional wisdom, would be more likely to increase the percentage of fat in the body and thus the prevalence of obesity. McNaughton et al. (2009) examined this possibility longitudinally using the AusDiab cohort data for 7441 male and female adult subjects. A 74-question instrument probed the dietary habits of the cohort in terms of calorie intake, snacking frequency and amount, empty calorie foods (extra foods), percent sugar and fat, and saturated fat. Salt and liquid intake was not included. When correcting for age, education, and energy misreporting, diet quality was significantly associated with fasting glucose (p = 0.05) and 2 hr. oral glucose tolerance (p = 0.01) levels in women, while fasting glucose (p < 0.0001) and glycosylated hemoglobin (p = 0.01) levels were significantly associated for men. If the lifestyle factors of smoking status, activity levels, and time spent in front of TV were also factored in, then diet quality was significantly associated with fasting glucose in men (p < 0.0001) and 2 hr. oral glucose tolerance (p < 0.0001) in women. Overall, a significant association between diet quality and diabetes risk was found for men only after correcting for age, education, and energy misreporting (p = 0.001), lifestyle (p = 0.03), and BMI (p 0.01). Diet quality for women predicted a trend (p = 0.08) towards developing prediabetes after correcting for age, education, and misreporting, but not for diabetes.

Metabolic Syndrome

Metabolic syndrome, which is a combination of insulin and glucose measures, in addition to lipid metabolism assessment, has a long history of empirical evidence supporting its diagnostic utility for predicting diabetes risk (reviewed by Cameron et al., 2008). Four definitions of metabolic syndrome were used to probe predictive power in the AusDiab cohort (N = 5842) over the five-year study period. In addition to these four definitions, the Diabetes Prediction Model (BPM) uses age, sex, ethnicity, fasting glucose, systolic blood pressure, HDL-C, BMI, and family history to predict diabetes risk. The relative risk for diabetes determined by the four metabolic syndrome definitions ranged between 1.7 and 2.4 for the AusDiab cohort (p < 0.001), after correcting for age and sex. DPM was at least as effective as the four...

Overall though, the best predictor of diabetes risk in this study was fasting glucose levels, with a relative risk of 3.05 (p < 0.0001) overall, and 2.56 (p < 0.0001) for individuals without metabolic syndrome.
Interaction between Diabetes Risk Factors

Obesity has long been recognized to be a significant risk factor for a number of diseases, including diabetes and cardiovascular disease. Diet quality, in terms of the relative consumption ratios between healthy foods like fruits and vegetables and unhealthy ones like pizza, soda, and candy, has a direct impact on obesity risk, and therefore diabetes risk. Diabetes is defined in part by elevated blood glucose levels and glucose intolerance, so metabolic syndrome, which is also defined in part by these measures, would be expected to be associated with diabetes risk. The interactions between obesity, diet quality, and metabolic syndrome are therefore significant.

Lifestyle Intervention

As revealed above, lifestyle choices modifies the risk of diabetes significantly. In an effort to determine whether lifestyle interventions could reduce diabetes severity, Cardona-Morrell et al. (2010) performed a meta-analysis of all studies examining this issue. A literature search identified 41 potentially relevant studies, but excluded all but 12 due to fundamental differences in the research protocol design and analysis methods. These 12 studies focused on people between the ages of 20 and 79, with most concerned with middle-aged adults. Outcome measures included weight, fasting glucose, waist size, 2 hr. glucose, diabetes incidence reduction, and behavior modification. When limiting the meta-analysis to randomized study subjects, only seven studies met this criterion. Of these, only four used similar outcome measures, units, and follow up period length, with a combined population size of 1,129 subjects. The statistically significant changes were a mean weight loss of 1.82 Kg and waist size reduction of 4.6 cm. When the indicators of diabetes were examined, fasting glucose levels were reduced by 0.19 mmol/l and oral glucose tolerance increased by 0.4 mmol/l. Unfortunately, these diabetes measures were not significantly different from controls. Cardona-Morrell et al. (2010) concluded that the small number of studies that have examined this question highlights the need to do more research in this area. It's important to note that these studies examined the effect of lifestyle changes on diabetes patients, rather than the risk of diabetes in a healthy population.

Medication and Pharmacist Interventions

As the above study revealed (Cameron et al., 2008) fasting blood glucose levels is one of the best predictors of diabetes risk. Lowering levels would therefore be expected to improve diabetes incidence rates. To assess the efficacy of community programs (DMAS) to improve patient compliance with medication treatment, 90 community pharmacies and their clients (N = 524) were monitored during 6-month and 12-month study periods for the number of times a pharmacist helped a patient better understand diabetes and its treatment (Krass et al., 2011). The mean age of the study population was 63 years of age and gender balanced. Although a number of medications were being used by this group, the majority were taking oral hypoglycaemics alone (69%). The mean age of non-compliant patients was younger (58 vs. 64; p < 0.001)), had higher levels of glycosylated hemoglobin (p = 0.04), and were less likely to self-monitor blood glucose levels (p < 0.002). This finding suggests compliance does modify treatment outcomes. During the course of the study and at a follow up exam 18 months after the start of the study, blood glucose, glycosylated hemoglobin, cholesterol, and triglycerides were all significantly reduced and stayed there. These results suggest medication compliance and pharmacist interventions can improve treatment outcomes for diabetes patients.

Conclusion

Although all risk factors for diabetes are probably not known, or even understood very well, the few discussed here are well researched and modifiable. The foundation for making real improvements in diabetes risk and treatment outcomes for a significant portion of the population is therefore possible. Obesity, diet quality, and metabolic syndrome represent diabetes risk factors that can be controlled in part through lifestyle changes and medication. For older adults who already have diabetes, the efficacy of the interventions discussed here appears to be considerably different, with medication and pharmacy interventions producing the biggest improvements in common diagnostic measures for disease severity. The findings of these studies also suggest diet modification and weight loss would lower diabetes risk significantly, if not produce slight improvements for diabetes patients.

Evaluation

Writing this case study report forced me to read multiple case studies and the appropriate background material,…