Characterization of the genetic landscape of Alzheimer’s disease (AD) and related dementias (ADD) provides a unique opportunity for a better understanding of the associated pathophysiological processes. We performed a two-stage genome-wide association study totaling 111,326 clinically diagnosed/‘proxy’ AD cases and 677,663 controls. We found 75 risk loci, of which 42 were new at the time of analysis. Pathway enrichment analyses confirmed the involvement of amyloid/tau pathways and highlighted microglia implication. Gene prioritization in the new loci identified 31 genes that were suggestive of new genetically associated processes, including the tumor necrosis factor alpha pathway through the linear ubiquitin chain assembly complex. We also built a new genetic risk score associated with the risk of future AD/dementia or progression from mild cognitive impairment to AD/dementia. The improvement in prediction led to a 1.6- to 1.9-fold increase in AD risk from the lowest to the highest decile, in addition to effects of age and the APOE ε4 allele.

Background: Stroke is associated with an increased risk of dementia; however, the impact of stroke on cognition has been found to be variable, such that stroke survivors can show decline, remain stable, or revert to baseline cognitive functioning. Knowing the natural history of cognitive impairment after stroke is important for intervention. The aim of this systematic review is to investigate the longitudinal course of cognitive function in stroke survivors. Methods and Results: Three electronic databases (Medline, Embase, PsycINFO) were searched using OvidSP from inception to July 15, 2016. Longitudinal studies with ≥2 time points of cognitive assessment after stroke were included. In total, 5952 articles were retrieved and 14 were included. There was a trend toward significant deterioration in cognitive test scores in stroke survivors (8 studies). Cognitive stability (3 studies) and improvement (3 studies) were also demonstrated, although follow‐up time tended to be shorter in these studies. Variables associated with impairment included age, ethnicity, premorbid cognitive performance, depression, stroke location, and history of previous stroke. Associations with APOE*E4 (apolipoprotein E with the E4 allele) allele status and sex were mixed. Conclusions: Stroke is associated with an increased risk of cognitive decline, but cognitive decline is not a consequence. Factors associated with decline, such as sociodemographic status, health‐related comorbidity, stroke history, and clinical features could be used in models to predict future risk of dementia after stroke. A risk model approach could identify patients at greatest risk for timely intervention to reduce the frequency or delay the onset of poststroke cognitive impairment and dementia.

Background: non-participation in epidemiological studies threatens the generalisability of findings. Objective: to investigate the change in non-participation between the Medical Research Council Cognitive Function and Ageing Study (CFAS) I and II. Design: a comparison of two epidemiological studies of older people using identical methods. Setting: three geographical areas of the United Kingdom. Subjects: older people aged 65 years and over. Methods: the two studies were conducted approximately two decades apart between 1989 and 1994 (CFAS I) and between 2008 and 2011 (CFAS II). Random samples were drawn from primary care lists. We compared demographic factors associated with non-participation. Results: non-participation in CFAS II was higher than in CFAS I (45.3 versus 18.3%). After adjustment for confounders, in both CFAS I and CFAS II, women were more likely to decline to take part (CFAS I: odds ratio (OR) 1.3 95% confidence interval (CI) 1.2 to 1.4; CFAS II: 1.1 95% CI 1.1 to 1.2). Deprivation was associated with non-participation in both studies (highest versus lowest Townsend deprivation quintile, CFAS I: OR 1.4 95% CI 1.2 to 1.6; CFAS II: 2.0 95% CI 1.8 to 2.2). Age was not associated with non-participation in either study (CFAS I, P = 0.21; CFAS II, P = 0.47). Conclusions: non-participation in epidemiological studies of older people has increased substantially in the past two decades and public willingness to take part in studies of this kind would appear to be declining. As communities become more diverse and older people have increasing commitments on their time, new ways to engage prospective participants are urgently needed.

Technology is widely promoted as a solution to greater independence and better health for the rapidly growing UK older population. If this is to be realised, we need to understand barriers and facilitators to uptake and investigate who wants this technology and who does not express an interest in use. This analysis is based on data from a population-based cohort study, the Cognitive Function and Ageing Study (CFAS)-II, which focused on brain health in older people and included questions about access to- and interest in- internet technologies. The factors affecting willingness to use technologies that support memory and ADL were identified using binary logistic regression analysis. 541 people aged 75 years and older from Cambridgeshire, Nottingham and Newcastle responded. Older adults were more willing to use technologies directed towards improving memory (65%) than towards ADL supportive technologies (38%). Regression analysis showed that an older age (OR = 0.64, 95% CI = 0.34–0.98), female gender (OR = 0.64, 95% CI = 0.42–0.99), no access to technology including laptops and tablets (OR = 0.48, 95% CI = 0.32–0.72), and self-reported physically less slowing down (but no objective health indicators) (OR = 0.57, 95% CI = 0.36–0.88) were strongly associated with UK older adults’ lesser willingness to use memory assistive technologies while not having access to laptops and tablets (OR = 0.57, 95% CI = 0.39–0.84) was associated with willingness to use ADL supportive technologies. Older people, females and those with less access to technologies should be considered as target groups by healthcare providers, policymakers, and technology producers to promote technology and support healthy and independent ageing.

With declining rates of participation in epidemiological studies there is an important need to attempt to understand what factors might affect response. This study examines the pattern of response at different adult ages within a contemporary cross-sectional population-based cohort, the Cambridge Centre for Ageing and Neuroscience (Cam-CAN). Using logistic regression, we investigated associations between age, gender and Townsend deprivation level for both participants and non-participants. Weighted estimates of the odds ratios with confidence intervals for each demographic characteristic were calculated. Reasons given for refusal were grouped into three broad categories: ‘active’, ‘passive’ and illness preventing interview. An association of age and participation was found, with individuals in middle age groups more likely to participate (age group 48–57 OR: 1.8, 95% CI: 1.5–2.2 and age group 58–67 OR: 2.1, 95% CI: 1.7–2.4). Overall, there was no difference in participation between men and women. An association with deprivation was found, with those living in the most deprived areas being the least willing to participate (fifth quintile OR: 0.6, 95% CI: 0.5–0.7). An interaction between age and gender was found whereby younger women and older men were more likely to agree to participate (p = 0.01). Our findings highlight some of the factors affecting recruitment into epidemiological studies in the UK and suggest that targeted age-specific recruitment strategies might be needed to increase participation rates in future cohort investigations.

This study examines variability across the age span in cognitive performance in a cross-sectional, population-based, adult lifespan cohort from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) study (n = 2680). A key question we highlight is whether using measures that are designed to detect age-related cognitive pathology may not be sensitive to, or reflective of, individual variability among younger adults. We present three issues that contribute to the debate for and against age-related increases in variability. Firstly, the need to formally define measures of central tendency and measures of variability. Secondly, in addition to the commonly addressed location-confounding (adjusting for covariates) there may exist changes in measures of variability due to confounder sub-groups. Finally, that increases in spread may be a result of floor or ceiling effects; where the measure is not sensitive enough at all ages. From the Cam-CAN study, a large population-based dataset, we demonstrate the existence of variability-confounding for the immediate episodic memory task; and show that increasing variance with age in our general cognitive measures is driven by a ceiling effect in younger age groups.