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Introduction: Identifying individuals at high risk of dementia is critical to optimized clinical care, formulating effective preventative strategies, and determining eligibility for clinical trials. Since our previous systematic reviews in 2010 and 2015, there has been a surge in dementia risk prediction modelling. The aim of this study was to update our previous reviews to explore, and critically review, new developments in dementia risk modelling. Methods: MEDLINE, Embase, Scopus, and Web of Science were searched from March 2014 to June 2022. Studies were included if they were population- or community-based cohorts (including electronic health record data), had developed a model for predicting late-life incident dementia, and included model performance indices such as discrimination, calibration, or external validation. Results: In total, 9,209 articles were identified from the electronic search, of which 74 met the inclusion criteria. We found a substantial increase in the number of new models published from 2014 (>50 new models), including an increase in the number of models developed using machine learning. Over 450 unique predictor (component) variables have been tested. Nineteen studies (26%) undertook external validation of newly developed or existing models, with mixed results. For the first time, models have also been developed in low- and middle-income countries (LMICs) and others validated in racial and ethnic minority groups. Conclusion: The literature on dementia risk prediction modelling is rapidly evolving with new analytical developments and testing in LMICs. However, it is still challenging to make recommendations about which one model is the most suitable for routine use in a clinical setting. There is an urgent need to develop a suitable, robust, validated risk prediction model in the general population that can be widely implemented in clinical practice to improve dementia prevention.
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Introduction: Cardiovascular diseases (CVDs) have been associated with an increased risk of dementia; yet the evidence is mixed. This review critically appraises and synthesises current evidence exploring associations between dementia risk and CVD and their risk factors, including coronary heart disease, heart failure, atrial fibrillation, hypertension, hyperlipidaemia, and arterial stiffness. Methods: MEDLINE, Embase, PsycINFO, and the Cochrane Database of Systematic Reviews were searched to identify systematic reviews with meta-analyses investigating the association between at least one of the CVDs of interest and dementia risk. The Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Systematic Reviews was used to assess methodological quality. Results: Twenty-five meta-analyses published between 2007 and 2021 were included. Studies largely consisted of cohorts from North America and Europe. Findings were variable, with coronary heart disease, heart failure, and atrial fibrillation consistently associated with increased risk for all-cause dementia, but results were inconsistent for Alzheimer's disease. Hypertension was more frequently associated with dementia during mid-life compared to late life. Findings concerning cholesterol were complex, and while results were inconsistent for low-density lipoprotein cholesterol and total cholesterol, there appeared to be no associations between triglycerides and high-density lipoprotein cholesterol. All meta-analyses investigating hypercholesterolaemia showed significant increases in dementia risk. There was a paucity of research on the association between arterial stiffness and dementia risk. Conclusion: Targeted CVD dementia prevention strategies could reduce dementia prevalence. Future research should determine the underpinning mechanisms linking heart and brain health to determine the most effective strategies for dementia risk reduction in CVD populations.