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.

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 is 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.

Identifying modifiable risk factors is crucial for dementia prevention, a global health concern. Depression is considered a risk factor for dementia, but the temporal dynamics across the life course remain inconclusive. Therefore, we aimed to systematically assess the relationship between the timing of depression assessment and risk of all-cause late-life dementia. We conducted an umbrella review and meta-analysis to assess incident dementia in individuals with non-current history of depression. PubMed and Ovid Embase, MEDLINE, and PsycInfo were searched from inception up to February 17, 2025. Systematic reviews with meta-analyses investigating the association between depression and incident late-life dementia were included. From eligible reviews, we also extracted data from studies reporting dementia risk as hazard ratios (HRs), analysing the timing of depression measurement using random-effects models for meta-analysis. This study is registered with PROSPERO, CRD42021249706. Of the 7763 records identified, nine reviews were eligible for inclusion of the umbrella review. One review was judged to be of moderate quality, while the others were either low (n = 3) or critically low (n = 5). For our meta-analyses, 18 studies reporting depression onset in later life (n = 901,762 participants, n = 7595 incident dementia cases) and seven studies on depression assessed during midlife (n ≥ 2,501,269 participants, n ≥ 276,929 incident dementia cases) were included. All studies in the meta-analyses were deemed to be of good quality, with no strong evidence of publication bias. Pooled HRs indicated depression present in late-life (HR 1.95, 95% CI: 1.68–2.26; I² = 77.5%) and midlife (HR 1.56, 95% CI: 1.12–2.18; I² = 97.5%) significantly increased risk of all-cause dementia. The findings suggest that depression across the life course may increase dementia risk; however, substantial heterogeneity and review quality should be considered when interpreting the strength of this evidence. A life course approach to the treatment and prevention of depression may help reduce the burden of dementia, but this will require scaling up access to effective mental health care for vulnerable populations. Further research is needed to clarify if the stronger late-life association reflects depression as an immediate risk factor or an early manifestation of neurodegenerative processes. 10.13039/501100000272 National Institute for Health and Care Research , 10.13039/100014013 UK Research and Innovation , and Saudi Arabian Cultural Mission.

Survivors of stroke are at a higher risk of cognitive syndromes, including dementia and delirium. Timely identification of those at-risk for cognitive syndromes could ensure better clinical management and implementation of risk reduction strategies. This study updates and appraises current evidence on prognostic accuracy of multicomponent risk models for post-stroke cognitive syndromes. In this updated systematic review, we searched multidisciplinary electronic databases between November 2019 and October 2024 for relevant studies. An updated search was conducted on May 30, 2025. Studies were included if they described a multicomponent risk prediction tool developed in a stroke population (aged ≥18 years), free of cognitive impairment/dementia at baseline, with no exclusions on language. All study designs of primary research were eligible provided the study reported a multicomponent model at any point to predict participant cognitive outcomes i.e., incident cognitive impairment, dementia or delirium. Multicomponent refers to having more than one feature in the model e.g. if the study only reported the discriminatory accuracy of a cognitive score this was not eligible. All studies had to report sufficient discriminative performance metrics to assess model performance. Data were extracted from selected studies using a pre-specified proforma. Risk of bias was assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST), certainty of evidence by GRADE, and between-study heterogeneity via I-squared (I²) statistics. Our study was preregistered with PROSPERO (CRD42024601845). From 16,259 articles, 20 new studies contributed 31 models for post-stroke cognitive impairment and/or dementia and six models for post-stroke delirium with most developed in Asia (n = 12). Most models (n = 10) used logistic regression, with some using machine learning methods (n = 5). Development cohorts were small (mean n = 677). The pooled c-statistic for post-stroke cognitive impairment and delirium were 0.81 (95% CI 0.77–0.85, I² 95.7%) and 0.85 (95% CI 0.77–0.93, I² 52.7%), respectively. Three models externally validated (C-statistic: 0.72–0.91); and two models underwent temporal validation (AUC 0.81–0.82). Eight studies included measures of calibration which all demonstrated good calibration. Most studies (n = 17) were deemed to have low risk of bias and applicability concerns but overall certainty of evidence by GRADE was low. Development of risk models to predict cognitive syndromes post-stroke has increased. Development cohorts remain small, largely developed in Asia with very few assessing model transportability. Future studies should pool data and utilise the potential of routinely collected large datasets. Stakeholder engagement and cost-effectiveness of risk-stratified interventions are needed prior to clinical implementation. National Institute for Health and Care Research Advanced Fellowship.

Background: Dementia is a major public health priority. Although there is abundant evidence of an association between dementia and poor cardiovascular health, findings have been inconsistent and uncertain in identifying which factors increase dementia risk in those with cardiovascular disease. Indeed, multiple variables including sociodemographic, economic, health, lifestyle and education may indicate who is at higher vs. lower dementia risk and could be used in prediction modelling. Therefore, the aim of this review is to synthesise evidence on the key risk factors for dementia in those with a history of cardiovascular disease. Methods: This is an overview of reviews protocol, registered on PROSPERO (CRD42021265363). Four electronic databases including MEDLINE, EMBASE, PsycINFO, and the Cochrane Database of Systematic Reviews will be searched. Studies will be included if they are systematic reviews and/or meta-analyses that have investigated the risk of incident dementia (all-cause and subtypes including Alzheimer’s disease and vascular dementia) in people with a history of coronary heart disease, heart failure, atrial fibrillation, hypertension, hyperlipidaemia, and vascular stiffness. Study selection will be completed by two independent researchers according to the eligibility criteria, and conflicts resolved by a third reviewer. References will be exported into Covidence for title and abstract sifting, full-text review, and data extraction. Methodological quality will be assessed using the AMSTAR-2 criteria and confidence of evidence will be assessed using the GRADE classification. This overview of reviews will follow PRISMA guidelines. If there is sufficient homogeneity in the data, the results will be pooled, and a meta-analysis conducted to determine the strength of association between each risk factor and incident all-cause dementia and its subtypes for each cardiovascular diagnoses separately. Discussion: We will create a comprehensive summary of the key risk factors linking cardiovascular diseases to risk of incident dementia. This knowledge is essential for informing risk predictive model development as well as the development of risk reduction and prevention strategies.

Dementia is a leading cause of disability and death globally. Individuals with diseases such as cardiovascular, cardiometabolic and cerebrovascular disease are often at increased dementia risk. However, while numerous models have been developed to predict dementia, they are often not tailored to disease-specific groups. Yet, different disease groups may have unique risk factor profiles and tailored models that account for these differences may have enhanced predictive accuracy. In this review, we synthesise findings from three previous systematic reviews on dementia risk model development and testing to present an overview of the literature on dementia risk prediction modelling in people with a history of disease. Nine studies met the inclusion criteria. Currently, disease-specific models have only been developed in people with a history of diabetes where demographic, disease-specific and comorbidity data were used. Some existing risk models, including CHA₂DS₂-VASc and CHADS₂, have been externally validated for dementia outcomes in those with atrial fibrillation and heart failure. One study developed a dementia risk model for their whole population, which had similar predictive accuracy when applied in a sub-sample with stroke. This emphasises the importance of considering disease status in identifying key predictors for dementia and generating accurate prediction models for dementia.