Geriatric syndromes encompass conditions associated with ageing and includes cognitive decline, functional disability, falls and fractures. The interplay between diabetes and geriatric syndromes is complex. On one hand, T1DM acts as a risk factor accelerating the onset of these conditions. Conversely, geriatric syndromes make self-management difficult, introducing challenges to achieving adequate glycaemic control. Nevertheless, integrating diabetic technologies in the management of these patients may help to overcome these challenges.
Cognitive decline in elderly individuals self-managing their diabetes necessitates a comprehensive evaluation. In adults with T1DM, cognition is primarily impacted by a slowing of mental speed and a reduction in mental flexibility (26). Interestingly, memory and learning abilities remain preserved in this cohort. Conversely, a 32 year follow up of individuals with T1DM in the DCCT/EDIC study revealed a notable decline in psychomotor, memory and mental efficiency with ageing (27). However, the absence of non-diabetic controls limits the meaningful interpretation of this study, impeding our ability to discern the specific impact of T1DM on the observed cognitive decline.
The risk of age-related cognitive dysfunction is influenced by both hypoglycaemia and HbA1C targets. The DCCT/EDIC study showed a 1% increase in average HbA1C resulted in a decrease in psychomotor efficiency, equivalent to 3.3 years of age (27). Furthermore, experiencing one or more severe hypoglycaemic events corresponded to a similar decrease in psychomotor efficiency (27). These findings contrast with an earlier 18-year follow up of the DCCT/EDIC study, assessing a cohort of younger individuals (28), which highlighted the significant impact of age on the correlation between glycaemic events and cognitive status. These findings suggest that more stringent HbA1c targets may be beneficial in preventing cognitive decline in T1DM in the elderly. However, conflicting evidence arises from studies implementing HbA1C cut-offs. Zheng et al. (29) found that maintaining HbA1C levels below 53.0 mmol/mol had no difference on cognitive decline compared to having a HbA1C above 53.0mmol/mol. Contrastingly, Luchsinger et al. observed a slowing of global cognitive decline in individuals maintaining a HbA1C below 53 mmol/mol (30). These disparities in findings may arise from the heterogeneity in the tools used for cognitive function assessment, and notable differences in follow-up durations. Moreover, Zheng et al. (29) applied multivariable adjustments for cognition, accounting for factors like age and education, unlike Luchsinger et al. (30). Notably, as neither study distinguished the type of diabetes, a notable gap in the literature persists. This underscores the need for interventional studies exploring the effect of HbA1C cut-offs in type 1 elderly diabetics on cognition as discussed previously.
As we integrate diabetic technologies in the elderly population, we must consider the impact of cognitive decline on an elderly person's ability to use this technology. In elderly individuals with T2DM, cognitive decline is detrimental to diabetes self-management and monitoring (31). This decline further contributes to poorer adherence to insulin therapy and more frequent episodes of hyperglycaemia (32) and, among insulin-dependent patients, results in a lack of understanding in hypoglycaemic management (33). Nevertheless, the influence of cognitive decline on T1DM monitoring and management is largely unexplored, especially considering the increasing use of insulin pens, CGM, and insulin pumps in T1DM patients. Cognitive dysfunction can lead to a multitude of adversities in day-to-day management of diabetes in an elderly patient (Figure 1), many of which need to be accounted for. Implementation of advanced HCLs hold promise for these individuals since they mitigate the need to calculate insulin doses. However, this technology still has multiple challenges, as discussed previously (23). Future research will need to assess the efficacy of fully autonomous closed loop systems in a population of elderly type 1 diabetics, including an assessment of its impacts on parameters such as HbA1C and frequency of hypoglycaemic and hyperglycaemic events.
A prominent risk associated with diabetes is hypoglycaemia. While emerging diabetes technologies may minimise this risk, the efficacy relies on the patient’s ability to navigate the technology. There is a mounting body of evidence that establishes a link between cognitive decline and an elevated risk of hypoglycaemia in the elderly. In a prospective study, Yaffe et al. (7) revealed a threefold increased risk of hypoglycaemic events in patients with diabetes and dementia, suggesting a bidirectional relationship between hypoglycaemia and cognitive decline. This creates a vicious cycle where hypoglycaemia chronically heightens the risk of cognitive decline (27,34) and cognitive decline acutely increases the risk of hypoglycaemia (7). These interlinked risk factors emphasise the need to minimise hypoglycaemic events to prevent both the exacerbation of cognitive decline and improve hypoglycaemic event outcomes. Consequently, there is a compelling case for the implementation of diabetes technology in this age group, potentially serving as a critical solution.