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Introduction: Risk prediction models aim to identify those at high risk to receive targeted interventions. We aimed to identify the proportion of future dementia cases that would be missed by a high-risk screening program., Methods: We identified validated dementia risk prediction models from systematic reviews. We applied these to European Prospective Investigation of Cancer Norfolk, a large population-based cohort of 30,387 individuals with 29 years of linked healthcare data., Results: A maximum of 16.0% (14.7,17.2) and 31.9% (30.2,33.5) of cases arose from the highest risk decile and quintiles, respectively. For every 1000 people considered to be at high risk, a maximum of 235 (215, 255) developed dementia., Discussion: Seven in every 10 cases of dementia arose from people at normal risk, and eight in every 10 people at high risk did not develop dementia. Individual-level prevention approaches targeted at high-risk groups are unlikely to produce large reductions in disease incidence at the population level., Highlights: Dementia, a significant public health challenge, is not an inevitability of aging; risk reduction is possible. Several dementia risk prediction models have been validated in the general population, and these aim to identify people at high risk of the disease who can then be targeted with primary prevention interventions. An alternative prevention approach is to focus on interventions that reduce risk across the population, irrespective of risk status. In our study, seven out of every ten people who developed dementia during 29 year follow-up were classed as 'normal-risk' (rather than 'high risk') at baseline. Eight out of every ten people who were at high risk at baseline did not go on to develop dementia. Even if effective, dementia risk reduction efforts based upon targeted high-risk approaches are unlikely to reduce incidence of disease at the population level., (© 2024 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Population-based proteomics offer a groundbreaking avenue to predict dementia onset. This study employed a proteome-wide, data-driven approach to investigate protein-dementia associations in 229 incident all-cause dementia (ACD) among 3,249 participants from the English Longitudinal Study of Ageing (ELSA) over a median 9.8-year follow-up, then validated in 1,506 incident ACD among 52,745 individuals from the UK Biobank (UKB) over median 13.7 years. NEFL and RPS6KB1 were robustly associated with incident ACD; MMP12 was associated with vascular dementia in ELSA. Additional markers EDA2R and KIM1 (HAVCR1) were identified from sensitivity analyses. Combining NEFL and RPS6KB1 with other factors yielded high predictive accuracy (area under the curve (AUC)=0.871) for incident ACD. Replication in the UKB confirmed associations between identified proteins with various dementia subtypes. Results from reverse Mendelian Randomization also supported the role of several proteins as early dementia biomarkers. These findings underscore proteomics' potential in identifying novel risk screening targets for dementia.