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Identification of individuals at high risk of dementia has usually focused attention on the clinical concept of mild cognitive impairment (MCI), which captures an intermediate state between normal cognitive ageing and dementia. In many countries age specific risk of dementia has declined, but whether this is also the case for subclinical cognitive impairment is unknown. This has important implications for prevention, planning and policy. Here we describe subclinical cognitive impairment and mild dementia prevalence changes, in the UK, over 2 decades. The Cognitive Function and Ageing Studies have examined the full spectrum of cognition, from normal to dementia, in representative populations of people aged ≥ 65 years in the UK over the last 2 decades 7635 participants were interviewed in CFAS I in Cambridgeshire, Newcastle, and Nottingham in 1991, with 1457 being diagnostically assessed. In the same geographical areas, the CFAS II investigators interviewed 7796 individuals in 2011. Using established criteria, the population was categorised into seven groups: no cognitive impairment, Mild cognitive Impairment (defined using consensus criteria), other cognitive impairment no dementia without functional impairment, OCIND with functional impairment, cognitive impairment (MMSE < 24 and no functional impairment), mild dementia (MMSE < 24 with functional impairment, not captured by CFAS dementia criteria), and CFAS dementia criteria. Multinomial logistic regression, adjusted for age and sex, was used to estimate the prevalence of impairment in both studies. Results were standardized to the age-sex specific UK and global population. There is a clear increase in the prevalence of other cognitive Impairment no Dementia (without functional impairment), with the purer MCI remaining stable. In the UK, mild dementia is estimated to fall from 520,704 cases (5.7%, 95% CI 3.8, 8.1) in 1991 to 315,142 (3.0%, 95% CI 2.4, 3.8) in 2011, cognitive impairment, has fallen from 1,225,984 (13.5%, 95% CI 10.1, 17.5) to 654,436 (6.3%, 95% CI 5.4, 7.3) cases. Using additional categories which reflect the continuum of cognitive decline and impairment in populations we see that the mildest dementia declines, but that there is stability in estimates of those who meet MCI criteria. Increases were found in the Other Cognitive Impairment no Dementia group. The decline observed in severe impairment thus seems to have resulted in larger proportions of the population in milder forms, seen alongside physical illnesses.

To systematically review the literature for dementia prediction models for use in the general population and externally validate their performance in a head-to-head comparison. We selected four prediction models for validation: CAIDE, BDSI, ANU-ADRI and DRS. From the Rotterdam Study, 6667 non-demented individuals aged 55 years and older were assessed between 1997 and 2001. Subsequently, participants were followed for dementia until 1 January, 2015. For each individual, we computed the risk of dementia using the reported scores from each prediction model. We used the C-statistic and calibration plots to assess the performance of each model to predict 10-year risk of all-cause dementia. For comparisons, we also evaluated discriminative accuracy using only the age component of these risk scores for each model separately. During 75,581 person-years of follow-up, 867 participants developed dementia. C-statistics for 10-year dementia risk prediction were 0.55 (95% CI 0.53-0.58) for CAIDE, 0.78 (0.76-0.81) for BDSI, 0.75 (0.74-0.77) for ANU-ADRI, and 0.81 (0.78-0.83) for DRS. Calibration plots showed that predicted risks were too extreme with underestimation at low risk and overestimation at high risk. Importantly, in all models age alone already showed nearly identical discriminative accuracy as the full model (C-statistics: 0.55 (0.53-0.58) for CAIDE, 0.81 (0.78-0.83) for BDSI, 0.77 (0.75-0.79) for ANU-ADRI, and 0.81 (0.78-0.83) for DRS). In this study, we found high variability in discriminative ability for predicting dementia in an elderly, community-dwelling population. All models showed similar discriminative ability when compared to prediction based on age alone. These findings highlight the urgent need for updated or new models to predict dementia risk in the general population.

Several studies have reported a decline in incidence of dementia which may have large implications for the projected burden of disease, and provide important guidance to preventive efforts. However, reports are conflicting or inconclusive with regard to the impact of gender and education with underlying causes of a presumed declining trend remaining largely unidentified. The Alzheimer Cohorts Consortium aggregates data from nine international population-based cohorts to determine changes in the incidence of dementia since 1990. We will employ Poisson regression models to calculate incidence rates in each cohort and Cox proportional hazard regression to compare 5-year cumulative hazards across study-specific epochs. Finally, we will meta-analyse changes per decade across cohorts, and repeat all analysis stratified by sex, education and APOE genotype. In all cohorts combined, there are data on almost 69,000 people at risk of dementia with the range of follow-up years between 2 and 27. The average age at baseline is similar across cohorts ranging between 72 and 77. Uniting a wide range of disease-specific and methodological expertise in research teams, the first analyses within the Alzheimer Cohorts Consortium are underway to tackle outstanding challenges in the assessment of time-trends in dementia occurrence.