ABSTRACT
The diabetic patient has a greater risk of impaired cognitive function (Frier 2011), depression (Wilfl ey et al. 2011; Lamers et al. 2011) and poor quality of life measures (Ribu 2009; Li and Ford 2009; Vijayakumar and Varghese 2009; Egede and Ellis 2010; Wilfl ey et al. 2011). Some specifi c symptoms such as nausea and vomiting or retinopathy will impact on those subjective symptoms related to quality of life measures (Mitchell and Bradley 2009; Mitchell et al. 2009; Jaffe et al. 2011; Mazhar et al. 2011). Poor quality of life may in turn impact on the physical health domains. For example diabetic patient with depression have a greater risk of being admitted to the intensive care unit (Davydow et al. 2011) and have reduced cognitive scores (Kadoi et al. 2011). The presence of diabetes as a co-morbid condition in schizophrenia and bipolar disorders also increases mortality (Vinogradova et al. 2010). The detailed interrelationships between these two-way processes between body and mind are yet to be elucidated. However, attempts are now being made to address these linkages. One contemporary example is the current Management and Impact for Long-Term Empowerment and Success (MILES) study being conducted in Australia (MILES 2011). This landmark investigation is the fi rst nationwide, indeed continent-wide study to examine the whole spectrum of the actual experience of living with diabetes, both Type 1 and Type 2 from the patients perspective. Importantly, the data capture approach to studies such as MILES enables a comprehensive account to be generated of both the everyday lived experience of the patients but also to develop a minimum data set that allows both change over time and the effect of interventions to be evaluated. Critically, the former gives a unique and person-centred context to evaluate the complex etiological aspects of both types of diabetes. Understanding these more complex interactions ultimately facilitates, not only a greater understanding of the underlying pathological process, but also emphasises the direct role of the patients themselves in terms of the effective management of the course of their disease. Moreover, the effects of interventions can be evaluated against a comprehensive baseline dataset, the implications of which include being able to establish the relative contribution of the intervention against a whole spectrum of clinically relevant psychosocial and biological
background variables. The extension of studies such as MILES to other countries using essentially the same and inclusive data collection and analysis strategy will also enable the discrete and often occluded genetic contribution to diabetes to be investigated, again within a contextually sensitive manner in which the relative contribution can be studied in detail. Borrowing from statistical techniques used in epidemiological research and psychometric evaluation of instruments such as structural equation modelling, sophisticated and causal models of etiology may be evaluated and more importantly, compared to determine, even within the context of complex explanatory models, the most parsimonious account of data.
