Potential impact of diabetes prevention on mortality and future burden of dementia and disability: a modelling study

In the first step, we examined four plausible future diabetes prevalence scenarios based on obesity prevalence projections. We assumed different futures for the currently increasing obesity trends—no change, a further acceleration, a halt, and finally, a reverse—and translated these into expected future trends in diabetes prevalence using the DPM [7]. We used this model to estimate possible future trends in diabetes prevalence given the four different futures for the currently increasing obesity trends, ranging from a 5 yearly change in the obesity prevalence of −3 to +5%. Each one of the projected trends in diabetes prevalence from 2015–2060 was used as a different scenario to be explored later in IMPACT-BAM (see electronic supplementary material [ESM] Chapter 4.4 and ESM Fig. 12).

The baseline scenario assumed a continuation of recent trends in leading diabetes drivers, mainly an increase in obesity prevalence of 1% every 5 years. This obesity trend corresponds roughly to the last 5 years of change in obesity reported in the Health Survey for England study [8]. For this trend, the DPM predicted a 26% rise in diabetes prevalence from 8.6% in 2015 to 10.8% in 2060.

Three additional scenarios, based on current trends in drivers of diabetes (a further acceleration, a halt, and a reverse in the current obesity trend) were evaluated against the baseline scenario. These scenarios were: (A) an increase in relative diabetes prevalence of 49%; (B) an increase by 20%; and (C) a slowing down of the increase to 7% by 2060. Details of all modelled scenarios are presented in Table 1 and ESM Chapter 4.4.

Our IMPACT-BAM baseline results (see Table 2) suggested that the number of disability incident cases among the population aged 65 and over is expected to increase from approximately 223,600 (95% UI 219,200–228,100) per year in 2015 to 288,100 (275,900–300,200) in 2045 and then fall slightly to 271,600 (253,900–287,400) in 2060. The number of incident dementia cases is also expected to rise from approximately 142,000 (137,000–147,300) per year in 2015 to 208,500 (197,400–218,900) in 2045 and 203,700 (189,100–217,500) in 2060.

This increase in the number of annual incident cases occurred despite a continuous decline in the incidence rates of dementia and disability. This suggests that the reason for the increasing number of projected new cases of dementia and disability is the projected ageing of the population, due to increasing longevity. The incidence of disability expressed per 1000 population aged ≥65 is forecasted to decrease from approximately 21.4 (95% UI 21.0–21.9) per 1000 person-years in 2015 to 17.1 (16.6–17.6) in 2045 and then 13.9 (13.3–14.4) in 2060. This decrease is also observed for dementia, with an expected fall from 13.6 (13.1–14.1) per 1000 person-years in 2015 to 12.4 (11.8–12.9) in 2045 and 10.4 (9.9–10.9) in 2060.

The projected total mortality rate in people aged ≥65 is predicted to decrease by approximately 39% between 2015 and 2060, from 38.7 (95% UI 38.3–39.2) to 23.6 (22.7–25.0) per 1000 population.

Our study suggests that a reduction in diabetes prevalence would result in a decline in age-specific incidence of dementia and disability and postpone these conditions to later years in life. However, trends in the incidence of dementia and disability at the population level would not be observed for a decade as a result of decreases in mortality and prolongation of life. Crucially, our study also suggests that substantial reductions in diabetes prevalence would compress morbidity, decreasing the percentage of life spent with disability by approximately 0.4% (see Fig. 3).

Our study is the first, to our knowledge, to estimate the potential effect of future diabetes trends on the future UK burden of disability and dementia while taking into account the complex population dynamics of morbidity and mortality.

Barnes and Yaffe [24] estimated the fraction of cases of Alzheimer’s disease attributable to diabetes by calculating its population attributable risk (PAR) as 2% worldwide and 3% in the USA. This corresponds to 81,000 and 17,000 fewer cases of dementia each year worldwide and in the USA, respectively, if diabetes prevalence decreases by 10%. Our results are far more conservative. It is likely that Barnes and Yaffe’s analysis over-estimated the potential burdens, because it did not account for any observed trends in risk factors, mortality or dementia, nor consider competing risks of mortality from CVD as opposed to dementia. Moreover, these analyses, based on simple PAR approaches, did not account for any relationship between diabetes duration and outcomes. Several studies have reported that the RR of fatal CHD increases in individuals with diabetes as the years lived with the disease increases [21, 23, 25].

The IMPACT-BAM model accounts for complex epidemiological interactions between several important sources of morbidity like CVD, dementia and disability, which share some risk factors. The structure of the model allowed us to take into account the risks between these conditions and competing risks of dying from cardiovascular and non-cardiovascular causes. Diabetes increases the risk of dying, not only from CVD, but also from many other diseases, including several cancers. Given the declining population risk of CVD mortality in future years, the competing risk of non-CVDs is expected to become critical. Our model accounts for this by separating projections of future cardiovascular and non-cardiovascular risk of death, which represents a substantial improvement on previous methodologies [24, 26]. Specifically, we account for trends in CVD- and non-CVD mortality and incidence, trends in dementia incidence, competing risks between cardiovascular and non-cardiovascular risk factors. Our methods also account for the increase in risk of both CVD and dementia caused by the time spent living with diabetes. IMPACT-BAM also models the time lag between newly developed diabetes and its complications. To our knowledge, there are no previous studies modelling the effect of prevention of diabetes on dementia and disability which also take into consideration the time needed to develop diabetes complications. This makes our estimates closer to real-life settings. Finally, our prediction model estimates the positive and negative effects of changes in diabetes prevalence. Our scenarios of possible future diabetes burden were based on reasonable assumptions of future obesity trends.

Our study also has limitations. Our approach does not account for any effect of change in the obesity distribution in the population not mediated through diabetes, including the effect on hypertension prevalence. This means that our results may be conservative. However, obesity is a less strong risk factor for dementia than diabetes [4].

We used the pre-existing model published by PHE, the DPM, to calculate expected trends in diabetes prevalence if the obesity prevalence increase accelerates (scenario A), stops (scenario B) or reverses (scenario C). Although obesity is widely recognised as the main driver of the current diabetes epidemic, diabetes trends can also be driven by many other risk factors that may have contrasting trends such as physical activity or pharmacological intervention in individuals with impaired glucose tolerance [27]. In addition, the DPM model (available on PHE official website) lacks information on any potential methodological limitations. Moreover, our approach to projecting the future distribution of the diabetes duration in the population does not account for increased risk of death due to longer duration of diabetes. This can lead to overestimation of the number of participants with longer duration of diabetes, and overestimating the total effect of a change in diabetes prevalence on dementia and disability incidence.

We used a multi-exposure, PARF approach to translate trends in diabetes prevalence to changes in the risk of dementia and cognitive impairment. This approach assumes causality of risk factor and disease. The evidence is still not as strong as it is for the association between some other risk factors and diseases. However, there is increasing evidence of the association between diabetes and dementia [4, 28‐31].

Finally, all the disability caused by conditions other than dementia and CVD is aggregated into a single model state. As a consequence, the transition probabilities of moving from this state to other states are not specific for these conditions, but rather represent an average probability for all other causes of disability. Nevertheless, because the ELSA sample is representative for the population of England and Wales, the estimated combined probabilities should be generalisable.

Our findings have implications for the medium and long term. With a 35-year perspective we might expect a shift in the burden of diabetes consequences, from CVD towards a much broader range of sequelae including dementia. Our study suggests that preventing diabetes is important, not only for future CVD, but also for the dementia and disability burden. Moreover, as CVD mortality continues to decline, we expect a further shift in the burden of diabetes to non-cardiovascular complications. Contrary to some other diabetes complications, such as nephropathy or retinopathy, people with cognitive decline and dementia cannot be offered treatment to significantly slow down the progress of the disease. Since an older population structure is the leading reason for the increase in the dementia burden, it is reasonable to expect that dementia will increase as a share of diabetes complications.

Constraining the diabetes epidemic could reduce dementia incidence in future decades, however the effect is likely to be gradual. The risk of CVD improves almost immediately in response to interventions that decrease serum cholesterol, BP or smoking [32]. In contrast, our study suggests a much longer time lag between decreases in population rates of obesity and diabetes, and subsequent reductions in the dementia and disability burden.

Evidence suggests that upstream, structural interventions are far more powerful prevention strategies, and also more likely to reduce inequalities, than individual level agentic interventions [33]. Several UK policies now aim to halt the rise in obesity and diabetes prevalence, including the Calorie Reduction Programme, the Sugar Reduction Programme, marketing campaigns, the Soft Drinks Industry Levy and National Diabetes Prevention Programme [11, 12, 34]. In terms of individual level interventions, the English NHS Health Check programme for adults aged 40–74, which already includes HbA_1c or fasting blood glucose to identify diabetes in people with high BMI and/or BP, has recently been updated to include dementia advice. This may be useful for patient education as recent research suggested nearly half of adults over 50 did not know that a healthy lifestyle could help prevent dementia [35].

Future reductions in diabetes prevalence could reduce the burden of disability and dementia, however these reductions in dementia and disability burden will be observed in the mid to long term. Other preventative interventions targeting the shared determinants of these and other non-communicable diseases must be harnessed to stem the rising tide of dementia and disability in England.