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Peer reviewed: True, Acknowledgements: The authors wish to thank Nicola Kimber and Abigail Britton from the MRC Epidemiology Unit, University of Cambridge, for continued support with EPIC-Norfolk data preparation and access., AIMS: We examine whether inclusion of artificial intelligence (AI)-enabled retinal vasculometry (RV) improves existing risk algorithms for incident stroke, myocardial infarction (MI) and circulatory mortality. METHODS: AI-enabled retinal vessel image analysis processed images from 88 052 UK Biobank (UKB) participants (aged 40-69 years at image capture) and 7411 European Prospective Investigation into Cancer (EPIC)-Norfolk participants (aged 48-92). Retinal arteriolar and venular width, tortuosity and area were extracted. Prediction models were developed in UKB using multivariable Cox proportional hazards regression for circulatory mortality, incident stroke and MI, and externally validated in EPIC-Norfolk. Model performance was assessed using optimism adjusted calibration, C-statistics and R2 statistics. Performance of Framingham risk scores (FRS) for incident stroke and incident MI, with addition of RV to FRS, were compared with a simpler model based on RV, age, smoking status and medical history (antihypertensive/cholesterol lowering medication, diabetes, prevalent stroke/MI). RESULTS: UKB prognostic models were developed on 65 144 participants (mean age 56.8; median follow-up 7.7 years) and validated in 5862 EPIC-Norfolk participants (67.6, 9.1 years, respectively). Prediction models for circulatory mortality in men and women had optimism adjusted C-statistics and R2 statistics between 0.75-0.77 and 0.33-0.44, respectively. For incident stroke and MI, addition of RV to FRS did not improve model performance in either cohort. However, the simpler RV model performed equally or better than FRS. CONCLUSION: RV offers an alternative predictive biomarker to traditional risk-scores for vascular health, without the need for blood sampling or blood pressure measurement. Further work is needed to examine RV in population screening to triage individuals at high-risk.

ObjectivesTo evaluate the characteristics of individuals recorded as having a dementia diagnosis in different routinely collected records and to examine the extent of overlap of dementia coding across data sources. Also, to present comparisons of secondary and primary care records providing value for researchers using routinely collected records for dementia outcome capture.Study designA prospective cohort study.Setting and participantsA cohort of 25 639 men and women in Norfolk, aged 40–79 years at recruitment (1993–1997) followed until 2018 linked to routinely collected to identify dementia cases. Data sources include mortality from death certification and National Health Service (NHS) hospital or secondary care records. Primary care records for a subset of the cohort were also reviewed.Primary outcome measureDiagnosis of dementia (any-cause).ResultsOver 2000 participants (n=2635 individuals) were found to have a dementia diagnosis recorded in one or more of the data sources examined. Limited concordance was observed across the secondary care data sources. We also observed discrepancies with primary care records for the subset and report on potential linkage-related selection bias.ConclusionsUse of different types of record linkage from varying parts of the UK’s health system reveals differences in recorded dementia diagnosis, indicating that dementia can be identified to varying extents in different parts of the NHS system. However, there is considerable variation, and limited overlap in those identified. We present potential selection biases that might occur depending on whether cause of death, or primary and secondary care data sources are used. With the expansion of using routinely collected health data, researchers must be aware of these potential biases and inaccuracies, reporting carefully on the likely extent of limitations and challenges of the data sources they use.

Additional file 3.

Additional file 2. Summary of included studies against each theme.

Additional file 1. Search terms.

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)

Although age is the strongest known risk factor, not all people who reach old age develop dementia before they die. Recommendations on potentially modifiable risk factors for the prevention of dementia are based on evidence that is, at best, moderate in strength. There are major calls to strengthen the evidence on potentially modifiable risk factors of dementia. The European Prospective Investigation into Cancer in Norfolk (EPIC-Norfolk) is a prospective population study of 25 639 men and women aged 40–79 years first recruited in 1993-1997, who attended a health examination. Subsequent follow-ups have involved self-report of health and lifestyle and further health examinations. Cognitive measures (7 tests assessing a range of domains) were introduced as part of a third health examination between 2006 and 2011 (including data from a pilot phase 2004–2006) and are available on 8585 individuals. Almost complete follow-up for disease outcomes, including dementia and mortality, has been established via linkage to health records. Education was strongly associated with cognitive function for all abilities tested. Cross-sectional and prospective analyses showed those who were physically inactive during work, were less likely to have poor cognition (bottom tenth percentile of a composite cognition score); Odds Ratio (OR) = 0.68 (95% Confidence Interval or CI 0.54, 0.86 P=0.001). In contrast, inactivity during leisure time was associated with increased risk of poor performance in the cross-sectional analyses, although this association was not observed in the prospective analyses. Poor cognition was independently associated with higher risk of all-cause mortality and predictive of incident dementia. Associations were observed for the composite score (global cognition) as well as specific cognitive abilities. Poor cognition in four or more tests was associated with ten-fold increased risk of developing dementia compared with those who did not perform poorly in any test OR=10.82 (95% CI 6.85, 17.10 P

© 2019 American Society for Nutrition. Background: In Mediterranean countries, adherence to a traditional Mediterranean dietary pattern (MedDiet) is associated with better cognitive function and reduced dementia risk. It is unclear if similar benefits exist in non-Mediterranean regions. Objectives: The aims of this study were to examine associations between MedDiet adherence and cognitive function in an older UK population and to investigate whether associations differed between individuals with high compared with low cardiovascular disease (CVD) risk. Methods: We conducted an analysis in 8009 older individuals with dietary data at Health Check 1 (1993-1997) and cognitive function data at Health Check 3 (2006-2011) of the European Prospective Investigation into Cancer and Nutrition-Norfolk (EPIC-Norfolk). Associations were explored between MedDiet adherence and global and domain-specific cognitive test scores and risk of poor cognitive performance in the entire cohort, and when stratified according to CVD risk status. Results: Higher MedDiet adherence defined by the Pyramid MedDiet score was associated with better global cognition (β ± SE = -0.012 ± 0.002; P < 0.001), verbal episodic memory (β ± SE = -0.009 ± 0.002; P < 0.001), and simple processing speed (β ± SE = -0.002 ± 0.001; P = 0.013). Lower risk of poor verbal episodic memory (OR: 0.784; 95% CI: 0.641, 0.959; P = 0.018), complex processing speed (OR: 0.739; 95% CI: 0.601, 0.907; P = 0.004), and prospective memory (OR: 0.841; 95% CI: 0.724, 0.977; P = 0.023) was also observed for the highest compared with the lowest Pyramid MedDiet tertiles. The effect of a 1-point increase in Pyramid score on global cognitive function was equivalent to 1.7 fewer years of cognitive aging. MedDiet adherence defined by the Mediterranean Diet Adherence Screener (MEDAS) score (mapped through the use of both binary and continuous scoring) showed similar, albeit less consistent, associations. In stratified analyses, associations were evident in individuals at higher CVD risk only (P < 0.05). Conclusions: Higher adherence to the MedDiet is associated with better cognitive function and lower risk of poor cognition in older UK adults. This evidence underpins the development of interventions to enhance MedDiet adherence, particularly in individuals at higher CVD risk, aiming to reduce the risk of age-related cognitive decline in non-Mediterranean populations.