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We envisage the development of new Brain Health Services to achieve primary and secondary dementia prevention. These services will complement existing memory clinics by targeting cognitively unimpaired individuals, where the focus is on risk profiling and personalized risk reduction interventions rather than diagnosing and treating late-stage disease. In this article, we review key potentially modifiable risk factors and genetic risk factors and discuss assessment of risk factors as well as additional fluid and imaging biomarkers that may enhance risk profiling. We then outline multidomain measures and risk profiling and provide practical guidelines for Brain Health Services, with consideration of outstanding uncertainties and challenges. Users of Brain Health Services should undergo risk profiling tailored to their age, level of risk, and availability of local resources. Initial risk assessment should incorporate a multidomain risk profiling measure. For users aged 39-64, we recommend the Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) Dementia Risk Score, whereas for users aged 65 and older, we recommend the Brief Dementia Screening Indicator (BDSI) and the Australian National University Alzheimer's Disease Risk Index (ANU-ADRI). The initial assessment should also include potentially modifiable risk factors including sociodemographic, lifestyle, and health factors. If resources allow, apolipoprotein E ɛ4 status testing and structural magnetic resonance imaging should be conducted. If this initial assessment indicates a low dementia risk, then low intensity interventions can be implemented. If the user has a high dementia risk, additional investigations should be considered if local resources allow. Common variant polygenic risk of late-onset AD can be tested in middle-aged or older adults. Rare variants should only be investigated in users with a family history of early-onset dementia in a first degree relative. Advanced imaging with 18-fluorodeoxyglucose positron emission tomography (FDG-PET) or amyloid PET may be informative in high risk users to clarify the nature and burden of their underlying pathologies. Cerebrospinal fluid biomarkers are not recommended for this setting, and blood-based biomarkers need further validation before clinical use. As new technologies become available, advances in artificial intelligence are likely to improve our ability to combine diverse data to further enhance risk profiling. Ultimately, Brain Health Services have the potential to reduce the future burden of dementia through risk profiling, risk communication, personalized risk reduction, and cognitive enhancement interventions.

We envisage the development of new Brain Health Services to achieve primary and secondary dementia prevention. These services will complement existing memory clinics by targeting cognitively unimpaired individuals, where the focus is on risk profiling and personalized risk reduction interventions rather than diagnosing and treating late-stage disease. In this article, we review key potentially modifiable risk factors and genetic risk factors and discuss assessment of risk factors as well as additional fluid and imaging biomarkers that may enhance risk profiling. We then outline multidomain measures and risk profiling and provide practical guidelines for Brain Health Services, with consideration of outstanding uncertainties and challenges. Users of Brain Health Services should undergo risk profiling tailored to their age, level of risk, and availability of local resources. Initial risk assessment should incorporate a multidomain risk profiling measure. For users aged 39–64, we recommend the Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) Dementia Risk Score, whereas for users aged 65 and older, we recommend the Brief Dementia Screening Indicator (BDSI) and the Australian National University Alzheimer’s Disease Risk Index (ANU-ADRI). The initial assessment should also include potentially modifiable risk factors including sociodemographic, lifestyle, and health factors. If resources allow, apolipoprotein E ɛ4 status testing and structural magnetic resonance imaging should be conducted. If this initial assessment indicates a low dementia risk, then low intensity interventions can be implemented. If the user has a high dementia risk, additional investigations should be considered if local resources allow. Common variant polygenic risk of late-onset AD can be tested in middle-aged or older adults. Rare variants should only be investigated in users with a family history of early-onset dementia in a first degree relative. Advanced imaging with 18-fluorodeoxyglucose positron emission tomography (FDG-PET) or amyloid PET may be informative in high risk users to clarify the nature and burden of their underlying pathologies. Cerebrospinal fluid biomarkers are not recommended for this setting, and blood-based biomarkers need further validation before clinical use. As new technologies become available, advances in artificial intelligence are likely to improve our ability to combine diverse data to further enhance risk profiling. Ultimately, Brain Health Services have the potential to reduce the future burden of dementia through risk profiling, risk communication, personalized risk reduction, and cognitive enhancement interventions.

Background: The current evidence for higher physical activity and better cognitive function and lower risk of dementia is strong but not conclusive. More robust evidence is needed to inform public health policy. We provide further insight to discrepancies observed across studies, reporting on habitual inactivity including that during work. Methods: We examined cross-sectional and prospective relationships of physical inactivity during leisure and occupation time, with cognitive performance using a validated physical activity index in a cohort of 8585 men and women aged 40-79 years at baseline (1993-1997) for different domains using a range of cognitive measures. Cognitive testing was conducted between 2006-2011 (including pilot phase 2004-2006). Associations were examined using multinomial logistic regression adjusting for socio-demographic and health variables as well total habitual physical activity. Results: Inactivity during work was inversely associated with poor cognitive performance (bottom tenth percentile of a composite cognition score); Odds Ratio (OR) = 0·68 (95% Confidence Interval (CI) 0.54, 0·86) P=0·001. Results were similar cross-sectionally; OR = 0·65 (95% CI 0·45, 0·93) P=0·02. Manual workers had increased risk of poor performance compared to those with an occupation classified as inactive. Inactivity during leisure time was associated with increased risk of poor performance in the cross-sectional analyses only. Conclusions: The relationship between inactivity and cognition is strongly confounded by education, social class and occupation. Physical activity during leisure may be protective for cognition, but work related physical activity is not protective. A greater understanding of the mechanisms and confounding underlying these paradoxical findings is needed., This work was supported by the Medical Research Council, UK (MRC) http://www.mrc.ac.uk/ (Ref: MR/N003284/1, MC-UU_12015/1 to N.W.); Cancer Research UK, http://www.cancerresearchuk.org/ (CRUK, Ref: C864/A8257) and NIHR, https://www.nihr.ac.uk (Ref: NF-SI-0616–10090 to C.B.). The clinic for EPIC- orfolk 3HC was funded by Research into Ageing, now known as Age UK, http://www.ageuk.org.uk/ (Grant Ref: 262). The pilot phase was supported by MRC (Ref: G9502233) and CRUK (Ref: C864/A2883)

Background Although ageing is strongly associated with cognitive decline, a wide range of cognitive ability is observed in older populations with varying rates of change across different cognitive domains. Methods Cognitive function was measured as part of the third health examination of the European Prospective Investigation of Cancer in Norfolk (EPIC-Norfolk 3) between 2006 and 2011 (including measures from the pilot phase from 2004 to 2006). This was done using a battery consisting of seven previously validated cognitive function tests assessing both global function and specific domains. The battery included a shortened version of the Extended Mental State Exam (SF-EMSE); letter cancellation task; Hopkins Verbal Learning Test (HVLT); Cambridge Neuropsychological Test Automated Battery Paired Associates Learning Test (CANTAB-PAL); Visual Sensitivity Test (VST); Shortened version of the National Adult Reading Test (Short-NART) and a task to test for prospective memory. We report the distribution of cognitive function in different cognitive domains by age and sex and compare the utility of a number of assessment tests in a general population of older men and women. Results Cognitive test data were available for 8585 men and women taking part in EPIC-Norfolk 3. Increasing age was generally associated with declining mean cognitive function, but there was a wide range observed within each age group as well as variability across different cognitive domains. Some sex differences were also observed. Conclusion Descriptive data are presented for this general population sample of older men and women. There is a wide range of cognitive performance seen in this population. Though average performance declines with age, there is large individual variability across different cognitive domains. These variations may provide insights into the determinants of cognitive function in later life. Electronic supplementary material The online version of this article (doi:10.1186/1471-2318-14-142) contains supplementary material, which is available to authorized users.

Epidemiologic studies have reported conflicting results on the relationship between daytime napping and mortality risk, and there are few data on the potential association in the British population. We investigated the associations between daytime napping and all-cause or cause-specific mortality in the European Prospective Investigation Into Cancer-Norfolk study, a British population-based cohort study. Among the 16,374 men and women who answered questions on napping habits between 1998 and 2000, a total of 3,251 died during the 13-year follow-up. Daytime napping was associated with an increased risk of all-cause mortality (for napping less than 1 hour per day on average, hazard ratio = 1.14, 95% confidence interval: 1.02, 1.27; for napping 1 hour or longer per day on average, hazard ratio = 1.32, 95% confidence interval: 1.04, 1.68), independent of age, sex, social class, educational level, marital status, employment status, body mass index, physical activity level, smoking status, alcohol intake, depression, self-reported general health, use of hypnotic drugs or other medications, time spent in bed at night, and presence of preexisting health conditions. This association was more pronounced for death from respiratory diseases (for napping less than 1 hour, hazard ratio = 1.40, 95% confidence interval: 0.95, 2.05; for napping 1 hour or more, hazard ratio = 2.56, 95% confidence interval: 1.34, 4.86) and in individuals 65 years of age or younger. Excessive daytime napping might be a useful marker of underlying health risk, particularly of respiratory problems, especially among those 65 years of age or younger. Further research is required to clarify the nature of the observed association.

We envisage the development of new Brain Health Services to achieve primary and secondary dementia prevention. These services will complement existing memory clinics by targeting cognitively unimpaired individuals, where the focus is on risk profiling and personalized risk reduction interventions rather than diagnosing and treating late-stage disease. In this article, we review key potentially modifiable risk factors and genetic risk factors and discuss assessment of risk factors as well as additional fluid and imaging biomarkers that may enhance risk profiling. We then outline multidomain measures and risk profiling and provide practical guidelines for Brain Health Services, with consideration of outstanding uncertainties and challenges. Users of Brain Health Services should undergo risk profiling tailored to their age, level of risk, and availability of local resources. Initial risk assessment should incorporate a multidomain risk profiling measure. For users aged 39���64, we recommend the Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) Dementia Risk Score, whereas for users aged 65 and older, we recommend the Brief Dementia Screening Indicator (BDSI) and the Australian National University Alzheimer���s Disease Risk Index (ANU-ADRI). The initial assessment should also include potentially modifiable risk factors including sociodemographic, lifestyle, and health factors. If resources allow, apolipoprotein E ��4 status testing and structural magnetic resonance imaging should be conducted. If this initial assessment indicates a low dementia risk, then low intensity interventions can be implemented. If the user has a high dementia risk, additional investigations should be considered if local resources allow. Common variant polygenic risk of late-onset AD can be tested in middle-aged or older adults. Rare variants should only be investigated in users with a family history of early-onset dementia in a first degree relative. Advanced imaging with 18-fluorodeoxyglucose positron emission tomography (FDG-PET) or amyloid PET may be informative in high risk users to clarify the nature and burden of their underlying pathologies. Cerebrospinal fluid biomarkers are not recommended for this setting, and blood-based biomarkers need further validation before clinical use. As new technologies become available, advances in artificial intelligence are likely to improve our ability to combine diverse data to further enhance risk profiling. Ultimately, Brain Health Services have the potential to reduce the future burden of dementia through risk profiling, risk communication, personalized risk reduction, and cognitive enhancement interventions.

We envisage the development of new Brain Health Services to achieve primary and secondary dementia prevention. These services will complement existing memory clinics by targeting cognitively unimpaired individuals, where the focus is on risk profiling and personalized risk reduction interventions rather than diagnosing and treating late-stage disease. In this article, we review key potentially modifiable risk factors and genetic risk factors and discuss assessment of risk factors as well as additional fluid and imaging biomarkers that may enhance risk profiling. We then outline multidomain measures and risk profiling and provide practical guidelines for Brain Health Services, with consideration of outstanding uncertainties and challenges. Users of Brain Health Services should undergo risk profiling tailored to their age, level of risk, and availability of local resources. Initial risk assessment should incorporate a multidomain risk profiling measure. For users aged 39–64, we recommend the Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) Dementia Risk Score, whereas for users aged 65 and older, we recommend the Brief Dementia Screening Indicator (BDSI) and the Australian National University Alzheimer’s Disease Risk Index (ANU-ADRI). The initial assessment should also include potentially modifiable risk factors including sociodemographic, lifestyle, and health factors. If resources allow, apolipoprotein E ɛ4 status testing and structural magnetic resonance imaging should be conducted. If this initial assessment indicates a low dementia risk, then low intensity interventions can be implemented. If the user has a high dementia risk, additional investigations should be considered if local resources allow. Common variant polygenic risk of late-onset AD can be tested in middle-aged or older adults. Rare variants should only be investigated in users with a family history of early-onset dementia in a first degree relative. Advanced imaging with 18-fluorodeoxyglucose positron emission tomography (FDG-PET) or amyloid PET may be informative in high risk users to clarify the nature and burden of their underlying pathologies. Cerebrospinal fluid biomarkers are not recommended for this setting, and blood-based biomarkers need further validation before clinical use. As new technologies become available, advances in artificial intelligence are likely to improve our ability to combine diverse data to further enhance risk profiling. Ultimately, Brain Health Services have the potential to reduce the future burden of dementia through risk profiling, risk communication, personalized risk reduction, and cognitive enhancement interventions.

Despite several studies demonstrating an independent and inverse association between cognition and mortality, the nature of this association still remains unclear. To examine the association of cognition and mortality after accounting for sociodemographic, health and lifestyle factors and to explore both test and population characteristics influencing this relationship. In a population based cohort of 8585 men and women aged 48-92 years, who had cognitive assessments in 2006-2011 and were followed up till 2016 for mortality, we examined the relationship between individual cognitive tests as well as a global cognition score to compare their ability in predicting mortality and whether these differed by population characteristics. Risk of death was estimated using Cox proportional hazard regression models including sociodemographic, lifestyle and health variables, and self-reported comorbidities, as covariates in the models. Poor cognitive performance (bottom quartile of combined cognition score) was associated with higher risk of mortality, Hazard Ratio = 1.32 (95% Confidence Interval 1.09, 1.60); individual cognitive tests varied in their mortality associations and also performed differently in middle-age and older age groups. Poor cognitive performance is independently associated with higher mortality. This association is observed for global cognition and for specific cognitive abilities. Associations vary depending on the cognitive test (and domain) as well as population characteristics, namely age and education.

We envisage the development of new Brain Health Services to achieve primary and secondary dementia prevention. These services will complement existing memory clinics by targeting cognitively unimpaired individuals, where the focus is on risk profiling and personalized risk reduction interventions rather than diagnosing and treating late-stage disease. In this article, we review key potentially modifiable risk factors and genetic risk factors and discuss assessment of risk factors as well as additional fluid and imaging biomarkers that may enhance risk profiling. We then outline multidomain measures and risk profiling and provide practical guidelines for Brain Health Services, with consideration of outstanding uncertainties and challenges. Users of Brain Health Services should undergo risk profiling tailored to their age, level of risk, and availability of local resources. Initial risk assessment should incorporate a multidomain risk profiling measure. For users aged 39-64, we recommend the Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) Dementia Risk Score, whereas for users aged 65 and older, we recommend the Brief Dementia Screening Indicator (BDSI) and the Australian National University Alzheimer's Disease Risk Index (ANU-ADRI). The initial assessment should also include potentially modifiable risk factors including sociodemographic, lifestyle, and health factors. If resources allow, apolipoprotein E ��4 status testing and structural magnetic resonance imaging should be conducted. If this initial assessment indicates a low dementia risk, then low intensity interventions can be implemented. If the user has a high dementia risk, additional investigations should be considered if local resources allow. Common variant polygenic risk of late-onset AD can be tested in middle-aged or older adults. Rare variants should only be investigated in users with a family history of early-onset dementia in a first degree relative. Advanced imaging with 18-fluorodeoxyglucose positron emission tomography (FDG-PET) or amyloid PET may be informative in high risk users to clarify the nature and burden of their underlying pathologies. Cerebrospinal fluid biomarkers are not recommended for this setting, and blood-based biomarkers need further validation before clinical use. As new technologies become available, advances in artificial intelligence are likely to improve our ability to combine diverse data to further enhance risk profiling. Ultimately, Brain Health Services have the potential to reduce the future burden of dementia through risk profiling, risk communication, personalized risk reduction, and cognitive enhancement interventions.

Introduction Musical instrument playing provides intellectual stimulation, which is hypothesised to generate cognitive reserve that protects against cognitive impairment. Studies to date have classified musicianship as a binary entity. This investigation draws on the dataset of the European Prospective Investigation of Cancer Norfolk study to examine the effect of frequency of playing on later-life cognition. Methods We compared three categorisations of self-reported musical playing frequency in late mid-life (12-month period) against cognitive performance measured after a 4–11 year delay, adjusted for relevant health and social confounders. Logistic regression models estimated the adjusted association between frequency of musical playing and the likelihood of being in the top and bottom cognitive deciles. Results A total of 5,693 participants (745 musicians) provided data on music playing, cognition and all co-variables. Classification of musicianship by frequency of playing demonstrated key differences in socio-demographic factors. Musicians outperformed non-musicians in cognition generally. Compared with non-musicians, frequent musicians had 80% higher odds of being in the top cognitive decile (OR 1.80 [95% CI 1.19–2.73]), whereas musicians playing at any frequency had 29% higher odds (95% CI 1.03–1.62). There was evidence of a threshold effect, rather than a linear dose–response relationship. Discussion This study supports a positive association between late mid-life musical instrument playing and later-life cognition, although causation cannot be assumed. Musicians playing frequently demonstrated the best cognition. ‘Musicians’ are a heterogeneous group and frequency of music playing seems a more informative measure than binary classification. Ideally, this more nuanced measure would be collected for different life course phases.

Despite several studies demonstrating an independent and inverse association between cognition and mortality, the nature of this association still remains unclear. To examine the association of cognition and mortality after accounting for sociodemographic, health and lifestyle factors and to explore both test and population characteristics influencing this relationship. In a population based cohort of 8585 men and women aged 48–92 years, who had cognitive assessments in 2006–2011 and were followed up till 2016 for mortality, we examined the relationship between individual cognitive tests as well as a global cognition score to compare their ability in predicting mortality and whether these differed by population characteristics. Risk of death was estimated using Cox proportional hazard regression models including sociodemographic, lifestyle and health variables, and self-reported comorbidities, as covariates in the models. Poor cognitive performance (bottom quartile of combined cognition score) was associated with higher risk of mortality, Hazard Ratio = 1.32 (95% Confidence Interval 1.09, 1.60); individual cognitive tests varied in their mortality associations and also performed differently in middle-age and older age groups. Poor cognitive performance is independently associated with higher mortality. This association is observed for global cognition and for specific cognitive abilities. Associations vary depending on the cognitive test (and domain) as well as population characteristics, namely age and education. Electronic supplementary material The online version of this article (10.1007/s10654-018-0439-z) contains supplementary material, which is available to authorized users.

The European Prospective Investigation of Cancer (EPIC) is a 10- country collaborative study in which EPIC-Norfolk is one of the UK centres. EPIC-Norfolk examined 25 639 men and women resident in East Anglia (aged 40–79 years), between 1993 and 1997. The EPIC collaboration was set up to examine the dietary determinants of cancer, but the remit in the EPIC-Norfolk cohort was broadened from the outset to include determinants of other health conditions and chronic diseases. EPIC-Norfolk completed a third round of health examinations (EPIC-Norfolk 3 or 3HC) in December 2011, on 8623 participants in the age range 48–92 years. EPIC-Norfolk focused on objective measures of cognitive function, physical cap- ability and visual health, adapting this existing mid-life cohort to the current need to investigate healthy and independent living for ageing societies. With a wealth of longitudinal data and a biobank (including DNA) collected at up to three separate time points, EPIC-Norfolk offers the unique opportunity to investigate the asso- ciation of lifestyle and biological factors, including genetic expos- ures, with a range of health outcomes in middle and later life. Information for data access can be found on the study website, details as given in this cohort profile