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A third of those over 80 years of age are likely to have dementia, the lack of a cure requires efforts directed at prevention and delaying the age of onset. We argue here for the importance of understanding the cognitive ageing process, seen as the decline in various cognitive functions from adulthood to old age. The impact of age on cognitive function is heterogeneous and the identification of risk factors associated with adverse cognitive ageing profiles would allow well-targeted interventions, behavioural or pharmacological, to delay and reduce the population burden of dementia. A shift away from binary outcomes such as dementia assessed at one point in time in elderly populations to research on cognitive ageing using repeated measures of cognitive function and starting earlier in the life course would allow the sources of variability in ageing to be better understood.
An effective treatment or cure for dementia remains elusive (Voelker 2008), making prevention and delaying the age of onset important in order to meet the public health challenge of an exponential increase in dementia. The 2009 World Alzheimer Report estimates 36 million cases of dementia in 2010 and projects that this number will double every 20 years, with most of the increase coming from low-income countries (Alzheimer’s Disease International 2009). Increase in life expectancy feeds the epidemic as the prevalence rises with age, doubling every 4–5 years after the age of 60 so that over a third of those over 80 are likely to have dementia (Ritchie 2002).
Dementia is a syndrome characterised by impairment of multiple cognitive capacities that are severe enough to interfere with daily functioning. The diagnosis is based on clinical rather than neuropathological status. As most cognitive functions decline with age, it is often difficult to establish where normal variation ends and the disease begins. However, there is now quite a lot of evidence from both a clinical and neurodegenerative point of view to suggest that dementia develops over many years, perhaps as long as 20–30 years (Alzheimer’s Disease International 2009; Nelson et al. 2009; Blennow et al. 2006). We argue here that attempts to identify risk factors need to take into account the length of the latency period and the insidious onset that characterises dementia. Besides age and genetic predisposition, several environmental and behavioural risk factors have been identified although these results lack consistency and specificity (Mayeux 2003). It is possible that inconsistent findings are a result of the research design used to study cognitive ageing; most studies on risk factors are based on elderly populations who may be free of clinical but not preclinical dementia at the start of follow-up.
Progress on the molecular basis for dementia, the amyloid hypothesis in particular (Hardy and Selkoe 2002), shows great promise. However, advances in understanding the dementia process are not yet at a stage that would allow biomarkers or changes in brain structure to be incorporated in the diagnostic criteria. Furthermore, the association between neuropathological aspects and clinical aspects in terms of neuropsychological functioning is not straight forward (Nelson et al. 2009). Autopsies of individuals with “normal” brain ageing and intact cognition reveal almost as many neurofibrillary tangles and amyloid plaques as are seen in patients with dementia (Crystal et al. 1988); 20–40% of non-demented individuals have enough neuropathology to warrant a diagnosis of dementia (Blennow et al. 2006). Both clinical and neuropathological studies suggest that dementia has a long preclinical phase characterised by progressive neuropathological changes and decline in cognitive functioning (Alzheimer’s Disease International 2009; Mayeux 2003). Population-based longitudinal studies can contribute by focussing on what causes the course of cognitive ageing in order to complete the long-term clinical picture of dementia. In the section that follows, we discuss the research paradigm we propose towards this end.
The shift away from binary outcomes measured at one point in time to an analysis of cognitive change using repeated measures of cognition brings substantial methodological challenges: What are the specific aspects of cognition that ought to be measured? How to ensure sound psychometric properties for cognitive tests? How to deal with learning and practise effects that plague repeat cognitive testing? What are the optimal statistical methods to analyse change, account for non-response in longitudinal analysis? Despite these challenges, the benefit of the focus on cognitive ageing is that the identification of risk factors for unfavourable ageing trajectories will help establish early therapeutic interventions. This has obvious advantages over treating individuals with dementia where neuropathological changes might well be too far advanced to be reversible.
The dementia epidemic requires strategic choices in setting research priorities to allow rapid translation into advances in clinical care. We recommend adoption of an extended time window for study of both cognitive ageing and risk factors in order to identify sub-clinical disease processes and novel risk factors at the earliest possible stages of life. This approach contrasts with the focus on elderly populations and attempts to contribute to the development of a better health-care delivery with earlier and more effective interventions in more accurately identified risk groups.
ASM is supported by a “European Young Investigator Award” from the European Science Foundation and MK by the Academy of Finland and the National Heart, Lung, and Blood Institute, NIH (R01HL036310). Both authors are supported by the National Institute on Aging, NIH (R01AG013196; R01AG034454).