Artificial intelligence for dementia drug discovery and trials optimization.

Drug discovery and clinical trial design for dementia have historically been challenging. In part these challenges have arisen from patient heterogeneity, length of disease course, and the tractability of a target for the brain. Applying big data analytics and machine learning tools for drug discovery and utilizing them to inform successful clinical trial design has the potential to accelerate progress. Opportunities arise at multiple stages in the therapy pipeline and the growing availability of large medical data sets opens possibilities for big data analyses to answer key questions in clinical and therapeutic challenges. However, before this goal is reached, several challenges need to be overcome and only a multi-disciplinary approach can promote data-driven decision-making to its full potential. Herein we review the current state of machine learning applications to clinical trial design and drug discovery, while presenting opportunities and recommendations that can break down the barriers to implementation.

Artificial intelligence for dementia research methods optimization.

Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high-dimensional data and our ability to translate these findings into improved patient outcomes. To improve reproducibility and replicability, researchers should make their well-documented code and modeling pipelines openly available. Data should also be shared where appropriate. To enhance the acceptability of models and AI-enabled systems to users, researchers should prioritize interpretable methods that provide insights into how decisions are generated. Models should be developed using multiple, diverse datasets to improve robustness, generalizability, and reduce potentially harmful bias. To improve clarity and reproducibility, researchers should adhere to reporting guidelines that are co-produced with multiple stakeholders. If these methodological challenges are overcome, AI and ML hold enormous promise for changing the landscape of dementia research and care. HIGHLIGHTS: Machine learning (ML) can improve diagnosis, prevention, and management of dementia. Inadequate reporting of ML procedures affects reproduction/replication of results. ML models built on unrepresentative datasets do not generalize to new datasets. Obligatory metrics for certain model structures and use cases have not been defined. Interpretability and trust in ML predictions are barriers to clinical translation.

Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia.

With the increase in large multimodal cohorts and high-throughput technologies, the potential for discovering novel biomarkers is no longer limited by data set size. Artificial intelligence (AI) and machine learning approaches have been developed to detect novel biomarkers and interactions in complex data sets. We discuss exemplar uses and evaluate current applications and limitations of AI to discover novel biomarkers. Remaining challenges include a lack of diversity in the data sets available, the sheer complexity of investigating interactions, the invasiveness and cost of some biomarkers, and poor reporting in some studies. Overcoming these challenges will involve collecting data from underrepresented populations, developing more powerful AI approaches, validating the use of noninvasive biomarkers, and adhering to reporting guidelines. By harnessing rich multimodal data through AI approaches and international collaborative innovation, we are well positioned to identify clinically useful biomarkers that are accurate, generalizable, unbiased, and acceptable in clinical practice. HIGHLIGHTS: Artificial intelligence and machine learning approaches may accelerate dementia biomarker discovery. Remaining challenges include data set suitability due to size and bias in cohort selection. Multimodal data, diverse data sets, improved machine learning approaches, real-world validation, and interdisciplinary collaboration are required.

Artificial intelligence for dementia-Applied models and digital health.

INTRODUCTION: The use of applied modeling in dementia risk prediction, diagnosis, and prognostics will have substantial public health benefits, particularly as "deep phenotyping" cohorts with multi-omics health data become available. METHODS: This narrative review synthesizes understanding of applied models and digital health technologies, in terms of dementia risk prediction, diagnostic discrimination, prognosis, and progression. Machine learning approaches show evidence of improved predictive power compared to standard clinical risk scores in predicting dementia, and the potential to decompose large numbers of variables into relatively few critical predictors. RESULTS: This review focuses on key areas of emerging promise including: emphasis on easier, more transparent data sharing and cohort access; integration of high-throughput biomarker and electronic health record data into modeling; and progressing beyond the primary prediction of dementia to secondary outcomes, for example, treatment response and physical health. DISCUSSION: Such approaches will benefit also from improvements in remote data measurement, whether cognitive (e.g., online), or naturalistic (e.g., watch-based accelerometry).

Artificial intelligence for dementia prevention.

INTRODUCTION: A wide range of modifiable risk factors for dementia have been identified. Considerable debate remains about these risk factors, possible interactions between them or with genetic risk, and causality, and how they can help in clinical trial recruitment and drug development. Artificial intelligence (AI) and machine learning (ML) may refine understanding. METHODS: ML approaches are being developed in dementia prevention. We discuss exemplar uses and evaluate the current applications and limitations in the dementia prevention field. RESULTS: Risk-profiling tools may help identify high-risk populations for clinical trials; however, their performance needs improvement. New risk-profiling and trial-recruitment tools underpinned by ML models may be effective in reducing costs and improving future trials. ML can inform drug-repurposing efforts and prioritization of disease-modifying therapeutics. DISCUSSION: ML is not yet widely used but has considerable potential to enhance precision in dementia prevention. HIGHLIGHTS: Artificial intelligence (AI) is not widely used in the dementia prevention field. Risk-profiling tools are not used in clinical practice. Causal insights are needed to understand risk factors over the lifespan. AI will help personalize risk-management tools for dementia prevention. AI could target specific patient groups that will benefit most for clinical trials.

Artificial intelligence for neurodegenerative experimental models.

INTRODUCTION: Experimental models are essential tools in neurodegenerative disease research. However, the translation of insights and drugs discovered in model systems has proven immensely challenging, marred by high failure rates in human clinical trials. METHODS: Here we review the application of artificial intelligence (AI) and machine learning (ML) in experimental medicine for dementia research. RESULTS: Considering the specific challenges of reproducibility and translation between other species or model systems and human biology in preclinical dementia research, we highlight best practices and resources that can be leveraged to quantify and evaluate translatability. We then evaluate how AI and ML approaches could be applied to enhance both cross-model reproducibility and translation to human biology, while sustaining biological interpretability. DISCUSSION: AI and ML approaches in experimental medicine remain in their infancy. However, they have great potential to strengthen preclinical research and translation if based upon adequate, robust, and reproducible experimental data. HIGHLIGHTS: There are increasing applications of AI in experimental medicine. We identified issues in reproducibility, cross-species translation, and data curation in the field. Our review highlights data resources and AI approaches as solutions. Multi-omics analysis with AI offers exciting future possibilities in drug discovery.

Artificial intelligence for dementia genetics and omics.

Genetics and omics studies of Alzheimer's disease and other dementia subtypes enhance our understanding of underlying mechanisms and pathways that can be targeted. We identified key remaining challenges: First, can we enhance genetic studies to address missing heritability? Can we identify reproducible omics signatures that differentiate between dementia subtypes? Can high-dimensional omics data identify improved biomarkers? How can genetics inform our understanding of causal status of dementia risk factors? And which biological processes are altered by dementia-related genetic variation? Artificial intelligence (AI) and machine learning approaches give us powerful new tools in helping us to tackle these challenges, and we review possible solutions and examples of best practice. However, their limitations also need to be considered, as well as the need for coordinated multidisciplinary research and diverse deeply phenotyped cohorts. Ultimately AI approaches improve our ability to interrogate genetics and omics data for precision dementia medicine. HIGHLIGHTS: We have identified five key challenges in dementia genetics and omics studies. AI can enable detection of undiscovered patterns in dementia genetics and omics data. Enhanced and more diverse genetics and omics datasets are still needed. Multidisciplinary collaborative efforts using AI can boost dementia research.

Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review.

INTRODUCTION: Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia. METHODS: We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases. RESULTS: A total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort. DISCUSSION: The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice. HIGHLIGHTS: There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias.

A comparison over 2 decades of disability-free life expectancy at age 65 years for those with long-term conditions in England: Analysis of the 2 longitudinal Cognitive Function and Ageing Studies.

BACKGROUND: Previous research has examined the improvements in healthy years if different health conditions are eliminated, but often with cross-sectional data, or for a limited number of conditions. We used longitudinal data to estimate disability-free life expectancy (DFLE) trends for older people with a broad number of health conditions, identify the conditions that would result in the greatest improvement in DFLE, and describe the contribution of the underlying transitions. METHODS AND FINDINGS: The Cognitive Function and Ageing Studies (CFAS I and II) are both large population-based studies of those aged 65 years or over in England with identical sampling strategies (CFAS I response 81.7%, N = 7,635; CFAS II response 54.7%, N = 7,762). CFAS I baseline interviews were conducted in 1991 to 1993 and CFAS II baseline interviews in 2008 to 2011, both with 2 years of follow-up. Disability was measured using the modified Townsend activities of daily living scale. Long-term conditions (LTCs-arthritis, cognitive impairment, coronary heart disease (CHD), diabetes, hearing difficulties, peripheral vascular disease (PVD), respiratory difficulties, stroke, and vision impairment) were self-reported. Multistate models estimated life expectancy (LE) and DFLE, stratified by sex and study and adjusted for age. DFLE was estimated from the transitions between disability-free and disability states at the baseline and 2-year follow-up interviews, and LE was estimated from mortality transitions up to 4.5 years after baseline. In CFAS I, 60.8% were women and average age was 75.6 years; in CFAS II, 56.1% were women and average age was 76.4 years. Cognitive impairment was the only LTC whose prevalence decreased over time (odds ratio: 0.6, 95% confidence interval (CI): 0.5 to 0.6, p < 0.001), and where the percentage of remaining years at age 65 years spent disability-free decreased for men (difference CFAS II-CFAS I: -3.6%, 95% CI: -8.2 to 1.0, p = 0.12) and women (difference CFAS II-CFAS I: -3.9%, 95% CI: -7.6 to 0.0, p = 0.04) with the LTC. For men and women with any other LTC, DFLE improved or remained similar. For women with CHD, years with disability decreased (-0.8 years, 95% CI: -3.1 to 1.6, p = 0.50) and DFLE increased (2.7 years, 95% CI: 0.7 to 4.7, p = 0.008), stemming from a reduction in the risk of incident disability (relative risk ratio: 0.6, 95% CI: 0.4 to 0.8, p = 0.004). The main limitations of the study were the self-report of health conditions and the response rate. However, inverse probability weights for baseline nonresponse and longitudinal attrition were used to ensure population representativeness. CONCLUSIONS: In this study, we observed improvements to DFLE between 1991 and 2011 despite the presence of most health conditions we considered. Attention needs to be paid to support and care for people with cognitive impairment who had different outcomes to those with physical health conditions.

New horizons for caring for people with dementia in hospital: the DEMENTIA CARE pointers for service change.

Approximately two-thirds of hospital admissions are older adults and almost half of these are likely to have some form of dementia. People with dementia are not only at an increased risk of adverse outcomes once admitted, but the unfamiliar environment and routinised practices of the wards and acute care can be particularly challenging for them, heightening their confusion, agitation and distress further impacting the ability to optimise their care. It is well established that a person-centred care approach helps alleviate some of the unfamiliar stress but how to embed this in the acute-care setting remains a challenge. In this article, we highlight the challenges that have been recognised in this area and put forward a set of evidence-based 'pointers for service change' to help organisations in the delivery of person-centred care. The DEMENTIA CARE pointers cover areas of: dementia awareness and understanding, education and training, modelling of person-centred care by clinical leaders, adapting the environment, teamwork (not being alone), taking the time to 'get to know', information sharing, access to necessary resources, communication, involving family (ask family), raising the profile of dementia care, and engaging volunteers. The pointers extend previous guidance, by recognising the importance of ward cultures that prioritise dementia care and institutional support that actively seeks to raise the profile of dementia care. The pointers provide a range of simple to more complex actions or areas for hospitals to help implement person-centred care approaches; however, embedding them within the organisational cultures of hospitals is the next challenge.

Radiographer-led discharge for emergency care patients, requiring projection radiography of minor musculoskeletal injuries: a scoping review.

BACKGROUND: Pressure on emergency departments (EDs) from increased attendance for minor injuries has been recognised in the United Kingdom. Radiographer-led discharge (RLD) has potential for improving efficiency, through radiographers trained to discharge patients or refer them for treatment at the point of image assessment. This review aims to scope all RLD literature and identify research assessing the merits of RLD and requirements to enable implementation. METHODS: We conducted a scoping review of studies relating to RLD of emergency care patients requiring projection radiography of minor musculoskeletal (MSK) injuries. MEDLINE, Embase and CINAHL, relevant radiography journals and grey literature were searched. Articles were reviewed and the full texts of selected studies were screened against eligibility criteria. The data were extracted, collated and a narrative synthesis completed. RESULTS: Seven studies with varying study designs were included in the review. The small number of studies was possibly due to a generally low research uptake in radiography. The main outcome for four studies was reduced length of stay in ED, with recall and re-attendance to ED a primary outcome in one study and secondary outcome for two other studies. The potential for increased efficiency in the minor MSK pathway patient pathway and capacity for ED staff was recognised. Radiographers identified a concern regarding the risk of litigation and incentive of increased salary when considering RLD. The studies were broadly radiographer focussed, despite RLD spanning ED and Radiology. CONCLUSION: There were a low number of RLD active radiographers, likely to be motivated individuals. However, RLD has potential for generalisability with protocol variations evident, all producing similar positive outcomes. Understanding radiography and ED culture could clarify facilitators for RLD to be utilised more sustainably into the future. Cost effectiveness studies, action research within ED, and cluster randomised controlled trial with process evaluation are needed to fully understand the potential for RLD. The cost effectiveness of RLD may provide financial support for training radiographers and increasing their salary, with potential future benefit of reduction in workload within ED. RLD implementation would require an inter-professional approach achieved by understanding ED staff and patient perspectives and ensuring these views are central to RLD implementation.

Activity interventions to improve the experience of care in hospital for people living with dementia: a systematic review.

BACKGROUND: An increasingly high number of patients admitted to hospital have dementia. Hospital environments can be particularly confusing and challenging for people living with dementia (Plwd) impacting their wellbeing and the ability to optimize their care. Improving the experience of care in hospital has been recognized as a priority, and non-pharmacological interventions including activity interventions have been associated with improved wellbeing and behavioral outcomes for Plwd in other settings. This systematic review aimed at evaluating the effectiveness of activity interventions to improve experience of care for Plwd in hospital. METHODS: Systematic searches were conducted in 16 electronic databases up to October 2019. Reference lists of included studies and forward citation searching were also conducted. Quantitative studies reporting comparative data for activity interventions delivered to Plwd aiming to improve their experience of care in hospital were included. Screening for inclusion, data extraction and quality appraisal were performed independently by two reviewers with discrepancies resolved by discussion with a third where necessary. Standardized mean differences (SMDs) were calculated where possible to support narrative statements and aid interpretation. RESULTS: Six studies met the inclusion criteria (one randomized and five non-randomized uncontrolled studies) including 216 Plwd. Activity interventions evaluated music, art, social, psychotherapeutic, and combinations of tailored activities in relation to wellbeing outcomes. Although studies were generally underpowered, findings indicated beneficial effects of activity interventions with improved mood and engagement of Plwd while in hospital, and reduced levels of responsive behaviors. Calculated SMDs ranged from very small to large but were mostly statistically non-significant. CONCLUSIONS: The small number of identified studies indicate that activity-based interventions implemented in hospitals may be effective in improving aspects of the care experience for Plwd. Larger well-conducted studies are needed to fully evaluate the potential of this type of non-pharmacological intervention to improve experience of care in hospital settings, and whether any benefits extend to staff wellbeing and the wider ward environment.

Association of Lifestyle and Genetic Risk With Incidence of Dementia.

IMPORTANCE: Genetic factors increase risk of dementia, but the extent to which this can be offset by lifestyle factors is unknown. OBJECTIVE: To investigate whether a healthy lifestyle is associated with lower risk of dementia regardless of genetic risk. DESIGN, SETTING, AND PARTICIPANTS: A retrospective cohort study that included adults of European ancestry aged at least 60 years without cognitive impairment or dementia at baseline. Participants joined the UK Biobank study from 2006 to 2010 and were followed up until 2016 or 2017. EXPOSURES: A polygenic risk score for dementia with low (lowest quintile), intermediate (quintiles 2 to 4), and high (highest quintile) risk categories and a weighted healthy lifestyle score, including no current smoking, regular physical activity, healthy diet, and moderate alcohol consumption, categorized into favorable, intermediate, and unfavorable lifestyles. MAIN OUTCOMES AND MEASURES: Incident all-cause dementia, ascertained through hospital inpatient and death records. RESULTS: A total of 196 383 individuals (mean [SD] age, 64.1 [2.9] years; 52.7% were women) were followed up for 1 545 433 person-years (median [interquartile range] follow-up, 8.0 [7.4-8.6] years). Overall, 68.1% of participants followed a favorable lifestyle, 23.6% followed an intermediate lifestyle, and 8.2% followed an unfavorable lifestyle. Twenty percent had high polygenic risk scores, 60% had intermediate risk scores, and 20% had low risk scores. Of the participants with high genetic risk, 1.23% (95% CI, 1.13%-1.35%) developed dementia compared with 0.63% (95% CI, 0.56%-0.71%) of the participants with low genetic risk (adjusted hazard ratio, 1.91 [95% CI, 1.64-2.23]). Of the participants with a high genetic risk and unfavorable lifestyle, 1.78% (95% CI, 1.38%-2.28%) developed dementia compared with 0.56% (95% CI, 0.48%-0.66%) of participants with low genetic risk and favorable lifestyle (hazard ratio, 2.83 [95% CI, 2.09-3.83]). There was no significant interaction between genetic risk and lifestyle factors (P = .99). Among participants with high genetic risk, 1.13% (95% CI, 1.01%-1.26%) of those with a favorable lifestyle developed dementia compared with 1.78% (95% CI, 1.38%-2.28%) with an unfavorable lifestyle (hazard ratio, 0.68 [95% CI, 0.51-0.90]). CONCLUSIONS AND RELEVANCE: Among older adults without cognitive impairment or dementia, both an unfavorable lifestyle and high genetic risk were significantly associated with higher dementia risk. A favorable lifestyle was associated with a lower dementia risk among participants with high genetic risk.

Stroke and dementia risk: A systematic review and meta-analysis.

INTRODUCTION: Stroke is an established risk factor for all-cause dementia, though meta-analyses are needed to quantify this risk. METHODS: We searched Medline, PsycINFO, and Embase for studies assessing prevalent or incident stroke versus a no-stroke comparison group and the risk of all-cause dementia. Random effects meta-analysis was used to pool adjusted estimates across studies, and meta-regression was used to investigate potential effect modifiers. RESULTS: We identified 36 studies of prevalent stroke (1.9 million participants) and 12 studies of incident stroke (1.3 million participants). For prevalent stroke, the pooled hazard ratio for all-cause dementia was 1.69 (95% confidence interval: 1.49-1.92; P < .00001; I2 = 87%). For incident stroke, the pooled risk ratio was 2.18 (95% confidence interval: 1.90-2.50; P < .00001; I2 = 88%). Study characteristics did not modify these associations, with the exception of sex which explained 50.2% of between-study heterogeneity for prevalent stroke. DISCUSSION: Stroke is a strong, independent, and potentially modifiable risk factor for all-cause dementia.

Coronary Artery Bypass Graft Surgery and Dementia Risk in the Cardiovascular Health Study.

INTRODUCTION: The association between history of coronary artery bypass graft surgery (CABG) and dementia risk remains unclear. METHODS: We conducted a prospective cohort analysis using data on 3155 elderly adults free from prevalent dementia from the US population-based Cardiovascular Health Study (CHS) with adjudicated incident all-cause dementia, Alzheimer disease (AD), vascular dementia (VaD), and mixed dementia. RESULTS: In the CHS, the hazard ratio (HR) for all-cause dementia was 1.93 [95% confidence interval (CI), 1.36-2.74] for those with CABG history compared with those with no CABG history after adjustment for potential confounders. Similar HRs were observed for AD (HR=1.71; 95% CI, 0.98-2.98), VaD (HR=1.42; 95% CI, 0.56-3.65), and mixed dementia (HR=2.73; 95% CI, 1.55-4.80). The same pattern of results was observed when these CHS findings were pooled with a prior prospective study, the pooled HRs were 1.96 (95% CI, 1.42-2.69) for all-cause dementia, 1.71 (95% CI, 1.04-2.79) for AD and 2.20 (95% CI, 0.78-6.19) for VaD. DISCUSSION: Our results suggest CABG history is associated with long-term dementia risk. Further investigation is warranted to examine the causal mechanisms which may explain this relationship or whether the association reflects differences in coronary artery disease severity.

Dissemination and implementation research in dementia care: a systematic scoping review and evidence map.

BACKGROUND: The need to better understand implementing evidence-informed dementia care has been recognised in multiple priority-setting partnerships. The aim of this scoping review was to give an overview of the state of the evidence on implementation and dissemination of dementia care, and create a systematic evidence map. METHODS: We sought studies that addressed dissemination and implementation strategies or described barriers and facilitators to implementation across dementia stages and care settings. Twelve databases were searched from inception to October 2015 followed by forward citation and grey literature searches. Quantitative studies with a comparative research design and qualitative studies with recognised methods of data collection were included. Titles, abstracts and full texts were screened independently by two reviewers with discrepancies resolved by a third where necessary. Data extraction was performed by one reviewer and checked by a second. Strategies were mapped according to the ERIC compilation. RESULTS: Eighty-eight studies were included (30 quantitative, 34 qualitative and 24 mixed-methods studies). Approximately 60% of studies reported implementation strategies to improve practice: training and education of professionals (94%), promotion of stakeholder interrelationships (69%) and evaluative strategies (46%) were common; financial strategies were rare (15%). Nearly 70% of studies reported barriers or facilitators of care practices primarily within residential care settings. Organisational factors, including time constraints and increased workload, were recurrent barriers, whereas leadership and managerial support were often reported to promote implementation. Less is known about implementation activities in primary care and hospital settings, or the views and experiences of people with dementia and their family caregivers. CONCLUSION: This scoping review and mapping of the evidence reveals a paucity of robust evidence to inform the successful dissemination and implementation of evidence-based dementia care. Further exploration of the most appropriate methods to evaluate and report initiatives to bring about change and of the effectiveness of implementation strategies is necessary if we are to make changes in practice that improve dementia care.

Risk Factors for Young-Onset Dementia in the UK Biobank.

Importance: There is limited information on modifiable risk factors for young-onset dementia (YOD). Objective: To examine factors that are associated with the incidence of YOD. Design, Setting, and Participants: This prospective cohort study used data from the UK Biobank, with baseline assessment between 2006 and 2010 and follow-up until March 31, 2021, for England and Scotland, and February 28, 2018, for Wales. Participants younger than 65 years and without a dementia diagnosis at baseline assessment were included in this study. Participants who were 65 years and older and those with dementia at baseline were excluded. Data were analyzed from May 2022 to April 2023. Exposures: A total of 39 potential risk factors were identified from systematic reviews of late-onset dementia and YOD risk factors and grouped into domains of sociodemographic factors (education, socioeconomic status, and sex), genetic factors (apolipoprotein E), lifestyle factors (physical activity, alcohol use, alcohol use disorder, smoking, diet, cognitive activity, social isolation, and marriage), environmental factors (nitrogen oxide, particulate matter, pesticide, and diesel), blood marker factors (vitamin D, C-reactive protein, estimated glomerular filtration rate function, and albumin), cardiometabolic factors (stroke, hypertension, diabetes, hypoglycemia, heart disease, atrial fibrillation, and aspirin use), psychiatric factors (depression, anxiety, benzodiazepine use, delirium, and sleep problems), and other factors (traumatic brain injury, rheumatoid arthritis, thyroid dysfunction, hearing impairment, and handgrip strength). Main Outcome and Measures: Multivariable Cox proportional hazards regression was used to study the association between the risk factors and incidence of YOD. Factors were tested stepwise first within domains and then across domains. Results: Of 356 052 included participants, 197 036 (55.3%) were women, and the mean (SD) age at baseline was 54.6 (7.0) years. During 2 891 409 person-years of follow-up, 485 incident YOD cases (251 of 485 men [51.8%]) were observed, yielding an incidence rate of 16.8 per 100 000 person-years (95% CI, 15.4-18.3). In the final model, 15 factors were significantly associated with a higher YOD risk, namely lower formal education, lower socioeconomic status, carrying 2 apolipoprotein ε4 allele, no alcohol use, alcohol use disorder, social isolation, vitamin D deficiency, high C-reactive protein levels, lower handgrip strength, hearing impairment, orthostatic hypotension, stroke, diabetes, heart disease, and depression. Conclusions and Relevance: In this study, several factors, mostly modifiable, were associated with a higher risk of YOD. These modifiable risk factors should be incorporated in future dementia prevention initiatives and raise new therapeutic possibilities for YOD.

Mediterranean diet, cognitive function, and dementia: a systematic review.

BACKGROUND: Adherence to a Mediterranean diet has been associated with lower risk of various age-related diseases including dementia. Although narrative reviews have been published, no systematic review has synthesized studies on the association between Mediterranean diet adherence and cognitive function or dementia. METHODS: We conducted a systematic review of 11 electronic databases (including Medline) of published articles up to January 2012. Reference lists, selected journal contents, and relevant websites were also searched. Study selection, data extraction, and quality assessment were performed independently by two reviewers using predefined criteria. Studies were included if they examined the association between a Mediterranean diet adherence score and cognitive function or dementia. RESULTS: Twelve eligible papers (11 observational studies and one randomized controlled trial) were identified, describing seven unique cohorts. Despite methodological heterogeneity and limited statistical power in some studies, there was a reasonably consistent pattern of associations. Higher adherence to Mediterranean diet was associated with better cognitive function, lower rates of cognitive decline, and reduced risk of Alzheimer disease in nine out of 12 studies, whereas results for mild cognitive impairment were inconsistent. CONCLUSIONS: Published studies suggest that greater adherence to Mediterranean diet is associated with slower cognitive decline and lower risk of developing Alzheimer disease. Further studies would be useful to clarify the association with mild cognitive impairment and vascular dementia. Long-term randomized controlled trials promoting a Mediterranean diet may help establish whether improved adherence helps to prevent or delay the onset of Alzheimer disease and dementia.