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.

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 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 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 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 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.

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 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 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.

PROTOCOL: Health and social care interventions in the 80 years old and over population: An evidence and gap map

This is the protocol for a Campbell systematic review. The objectives are as follows: identify available systematic reviews and randomised controlled trials on interventions targeting health or social needs of the people aged over 80; identify qualitative studies relating to the experiences of people aged over 80 of interventions that target their health or social needs; identify areas where systematic reviews are needed; identify gaps in evidence where further primary research is needed; assess equity considerations (using the PROGRESS plus criteria) in available systematic reviews, randomised trials and qualitative studies of identified interventions; assess gaps and evidence related to health equity.

Artificial Intelligence for Dementia Research Methods Optimization

INTRODUCTION

Machine learning (ML) has been extremely successful in identifying key features from high-dimensional datasets and executing complicated tasks with human expert levels of accuracy or greater.

METHODS

We summarize and critically evaluate current applications of ML in dementia research and highlight directions for future research.

RESULTS

We present an overview of ML algorithms most frequently used in dementia research and highlight future opportunities for the use of ML in clinical practice, experimental medicine, and clinical trials. We discuss issues of reproducibility, replicability and interpretability and how these impact the clinical applicability of dementia research. Finally, we give examples of how state-of-the-art methods, such as transfer learning, multi-task learning, and reinforcement learning, may be applied to overcome these issues and aid the translation of research to clinical practice in the future.

DISCUSSION

ML-based models hold great promise to advance our understanding of the underlying causes and pathological mechanisms of dementia.

Harnessing the potential of machine learning and artificial intelligence for dementia research

Progress in dementia research has been limited, with substantial gaps in our knowledge of targets for prevention, mechanisms for disease progression, and disease-modifying treatments. The growing availability of multimodal data sets opens possibilities for the application of machine learning and artificial intelligence (AI) to help answer key questions in the field. We provide an overview of the state of the science, highlighting current challenges and opportunities for utilisation of AI approaches to move the field forward in the areas of genetics, experimental medicine, drug discovery and trials optimisation, imaging, and prevention. Machine learning methods can enhance results of genetic studies, help determine biological effects and facilitate the identification of drug targets based on genetic and transcriptomic information. The use of unsupervised learning for understanding disease mechanisms for drug discovery is promising, while analysis of multimodal data sets to characterise and quantify disease severity and subtype are also beginning to contribute to optimisation of clinical trial recruitment. Data-driven experimental medicine is needed to analyse data across modalities and develop novel algorithms to translate insights from animal models to human disease biology. AI methods in neuroimaging outperform traditional approaches for diagnostic classification, and although challenges around validation and translation remain, there is optimism for their meaningful integration to clinical practice in the near future. AI-based models can also clarify our understanding of the causality and commonality of dementia risk factors, informing and improving risk prediction models along with the development of preventative interventions. The complexity and heterogeneity of dementia requires an alternative approach beyond traditional design and analytical approaches. Although not yet widely used in dementia research, machine learning and AI have the potential to unlock current challenges and advance precision dementia medicine.

Ocular Biomarkers for Alzheimer Disease Dementia: An Umbrella Review of Systematic Reviews and Meta-analyses

Importance

Several ocular biomarkers have been proposed for the early detection of Alzheimer disease (AD) and mild cognitive impairment (MCI), particularly fundus photography, optical coherence tomography (OCT), and OCT angiography (OCTA).

Objective

To perform an umbrella review of systematic reviews to assess the diagnostic accuracy of ocular biomarkers for early diagnosis of Alzheimer disease.

Data Sources

MEDLINE, Embase, and PsycINFO were searched from January 2000 to November 2021. The references of included reviews were also searched.

Study Selection

Systematic reviews investigating the diagnostic accuracy of ocular biomarkers to detect AD and MCI, in secondary care or memory clinics, against established clinical criteria or clinical judgment.

Data Extraction and Synthesis

The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline checklist was followed and the Risk Of Bias in Systematic reviews tool was used to assess review quality.

Main Outcomes and Measures

The prespecified outcome was the accuracy of ocular biomarkers for diagnosing AD and MCI. The area under the curve (AUC) was derived from standardized mean difference.

Results

From the 591 titles, 14 systematic reviews were included (median [range] number of studies in each review, 14 [5-126]). Only 4 reviews were at low risk of bias on all Risk of Bias in Systematic Reviews domains. The imaging-derived parameters with the most evidence for detecting AD compared with healthy controls were OCT peripapillary retinal nerve fiber layer thickness (38 studies including 1883 patients with AD and 2510 controls; AUC = 0.70; 95% CI, 0.53-0.79); OCTA foveal avascular zone (5 studies including 177 patients with AD and 371 controls; AUC = 0.73; 95% CI, 0.50-0.89); and saccadic eye movements prosaccade latency (30 studies including 651 patients with AD/MCI and 771 controls; AUC = 0.64; 95% CI, 0.58-0.69). Antisaccade error was investigated in fewer studies (12 studies including 424 patients with AD/MCI and 382 controls) and yielded the best accuracy (AUC = 0.79; 95% CI, 0.70-0.88).

Conclusions and Relevance

This umbrella review has highlighted limitations in design and reporting of the existing research on ocular biomarkers for diagnosing AD. Parameters with the best evidence showed poor to moderate diagnostic accuracy in cross-sectional studies. Future longitudinal studies should investigate whether changes in OCT and OCTA measurements over time can yield accurate predictions of AD onset.

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.

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.

The impact of long-term conditions on disability-free life expectancy: A systematic review

Although leading causes of death are regularly reported, there is disagreement on which long-term conditions (LTCs) reduce disability-free life expectancy (DFLE) the most. We aimed to estimate increases in DFLE associated with elimination of a range of LTCs. This is a comprehensive systematic review and meta-analysis of studies assessing the effects of LTCs on health expectancy (HE). MEDLINE, Embase, HMIC, Science Citation Index, and Social Science Citation Index were systematically searched for studies published in English from July 2007 to July 2020 with updated searches from inception to April 8, 2021. LTCs considered included: arthritis, diabetes, cardiovascular disease including stroke and peripheral vascular disease, respiratory disease, visual and hearing impairment, dementia, cognitive impairment, depression, cancer, and comorbidity. Studies were included if they estimated HE outcomes (disability-free, active or healthy life expectancy) at age 50 or older for individuals with and without the LTC. Study selection and quality assessment were undertaken by teams of independent reviewers. Meta-analysis was feasible if three or more studies assessed the impact of the same LTC on the same HE at the same age using comparable methods, with narrative syntheses for the remaining studies. Studies reporting Years of Life Lost (YLL), Years of Life with Disability (YLD) and Disability Adjusted Life Years (DALYs = YLL+YLD) were included but reported separately as incomparable with other HE outcomes (PROSPERO registration: CRD42020196049). Searches returned 6072 unique records, yielding 404 eligible for full text retrieval from which 30 DFLE-related and 7 DALY-related were eligible for inclusion. Thirteen studies reported a single condition, and 17 studies reported on more than one condition (two to nine LTCs). Only seven studies examined the impact of comorbidities. Random effects meta-analyses were feasible for a subgroup of studies examining diabetes (four studies) or respiratory diseases (three studies) on DFLE. From pooled results, individuals at age 65 without diabetes gain on average 2.28 years disability-free compared to those with diabetes (95% CI: 0.57-3.99, p<0.01, I2 = 96.7%), whilst individuals without respiratory diseases gain on average 1.47 years compared to those with respiratory diseases (95% CI: 0.77-2.17, p<0.01, I2 = 79.8%). Eliminating diabetes, stroke, hypertension or arthritis would result in compression of disability. Of the seven longitudinal studies assessing the impact of multiple LTCs, three found that stroke had the greatest effect on DFLE for both genders. This study is the first to systematically quantify the impact of LTCs on both HE and LE at a global level, to assess potential compression of disability. Diabetes, stroke, hypertension and arthritis had a greater effect on DFLE than LE and so elimination would result in compression of disability. Guidelines for reporting HE outcomes would assist data synthesis in the future, which would in turn aid public health policy.

The contribution of multiple long-term conditions to widening inequalities in disability-free life expectancy over two decades: Longitudinal analysis of two cohorts using the Cognitive Function and Ageing Studies

BACKGROUND

: Disability-free life expectancy (DFLE) inequalities by socioeconomic deprivation are widening, alongside rising prevalence of multiple long-term conditions (MLTCs). We use longitudinal data to assess whether MLTCs contribute to the widening DFLE inequalities by socioeconomic deprivation.

METHODS

: The Cognitive Function and Ageing Studies (CFAS I and II) are large population-based studies of those ≥65 years, conducted in three areas in England. Baseline occurred in 1991 (CFAS I, n=7635) and 2011 (CFAS II, n=7762) with two-year follow-up. We defined disability as difficulty in activities of daily living, MLTCs as the presence of at least two of nine health conditions, and socioeconomic deprivation by area-level deprivation tertiles. DFLE and transitions between disability states and death were estimated from multistate models.

FINDINGS

: For people with MLTCs, inequalities in DFLE at age 65 between the most and least affluent widened to around 2.5 years (men:2.4 years, 95% confidence interval (95%CI) 0.4-4.4; women:2.6 years, 95%CI 0.7-4.5) by 2011. Incident disability reduced for the most affluent women (Relative Risk Ratio (RRR):0.6, 95%CI 0.4-0.9), and mortality with disability reduced for least affluent men (RRR:0.6, 95%CI 0.5-0.8). MLTCs prevalence increased only for least affluent men (1991: 58.8%, 2011: 66.9%) and women (1991: 60.9%, 2011: 69.1%). However, DFLE inequalities were as large in people without MLTCs (men:2.4 years, 95%CI 0.3-4.5; women:3.1 years, 95% CI 0.8-5.4).

INTERPRETATION

: Widening DFLE inequalities were not solely due to MLTCs. Reduced disability incidence with MLTCs is possible but was only achieved in the most affluent.

Understanding and improving experiences of care in hospital for people living with dementia, their carers and staff: three systematic reviews

Background

Being in hospital can be particularly confusing and challenging not only for people living with dementia, but also for their carers and the staff who care for them. Improving the experience of care for people living with dementia in hospital has been recognised as a priority.

Objectives

To understand the experience of care in hospital for people living with dementia, their carers and the staff who care for them and to assess what we know about improving the experience of care.

Review methods

We undertook three systematic reviews: (1) the experience of care in hospital, (2) the experience of interventions to improve care in hospital and (3) the effectiveness and cost-effectiveness of interventions to improve the experience of care. Reviews 1 and 2 sought primary qualitative studies and were analysed using meta-ethnography. Review 3 sought comparative studies and economic evaluations of interventions to improve experience of care. An interweaving approach to overarching synthesis was used to integrate the findings across the reviews.

Data sources

Sixteen electronic databases were searched. Forwards and backwards citation chasing, author contact and grey literature searches were undertaken. Screening of title and abstracts and full texts was performed by two reviewers independently. A quality appraisal of all included studies was undertaken.

Results

Sixty-three studies (reported in 82 papers) were included in review 1, 14 studies (reported in 16 papers) were included in review 2, and 25 studies (reported in 26 papers) were included in review 3. A synthesis of review 1 studies found that when staff were delivering more person-centred care, people living with dementia, carers and staff all experienced this as better care. The line of argument, which represents the conceptual findings as a whole, was that ‘a change of hospital culture is needed before person-centred care can become routine’. From reviews 2 and 3, there was some evidence of improvements in experience of care from activities, staff training, added capacity and inclusion of carers. In consultation with internal and external stakeholders, the findings from the three reviews and overarching synthesis were developed into 12 DEMENTIA CARE pointers for service change: key institutional and environmental practices and processes that could help improve experience of care for people living with dementia in hospital.

Limitations

Few of the studies explored experience from the perspectives of people living with dementia. The measurement of experience of care across the studies was not consistent. Methodological variability and the small number of intervention studies limited the ability to draw conclusions on effectiveness.

Conclusions

The evidence suggests that, to improve the experience of care in hospital for people living with dementia, a transformation of organisational and ward cultures is needed that supports person-centred care and values the status of dementia care. Changes need to cut across hierarchies and training systems to facilitate working patterns and interactions that enable both physical and emotional care of people living with dementia in hospital. Future research needs to identify how such changes can be implemented, and how they can be maintained in the long term. To do this, well-designed controlled studies with improved reporting of methods and intervention details to elevate the quality of available evidence and facilitate comparisons across different interventions are required.

Study registration

This study is registered as PROSPERO CRD42018086013.

Funding

This project was funded by the National Institute for Health Research (NIHR) Health Services and Delivery Research programme and will be published in full in Health Services and Delivery Research; Vol. 8, No. 43. See the NIHR Journals Library website for further project information. Additional funding was provided by the NIHR Collaboration for Leadership in Applied Health Research and Care South West Peninsula.

The experiences of hospital staff who provide care for people living with dementia: A systematic review and synthesis of qualitative studies

AIMS AND OBJECTIVES

To systematically review and synthesise qualitative data from studies exploring the experiences of hospital staff who care for people living with dementia (Plwd).

BACKGROUND

In hospital, the number of Plwd continues to rise; however, their experiences of care remain problematic. Negative experiences of care are likely to contribute to poorer mental and physical health outcomes for Plwd while in hospital and after discharge. Experiences of the hospital staff who care for Plwd can also be poor or unrewarding. It is important to understand the experiences of staff in order to improve staff well-being and ultimately the experience of care for Plwd while in hospital.

DESIGN

Systematic review and evidence synthesis of qualitative research.

DATA SOURCES

We searched 16 electronic databases in March 2018 and completed forward and backward citation chasing.

METHODS

Eligible studies explored the experiences of paid and unpaid staff providing care in hospital for Plwd. Study selection was undertaken independently by two reviewers, and quality appraisal was conducted. We prioritised included studies according to richness of text, methodological rigour and conceptual contribution. We adopted approaches of meta-ethnography to analyse study findings, creating a conceptual model to represent the line of argument.

FINDINGS

Forty-five studies reported in 58 papers met the inclusion criteria, and of these, we prioritised 19 studies reported in 24 papers. The line of argument was that Institutions can improve staff experiences of care for Plwd by fostering person-centred care (PCC). PCC aligned with staff perceptions of 'good care'; however, staff often felt prevented from providing PCC because of care cultures that prioritised tasks, routines and physical health. Staff experienced conflict over the care they wanted to give versus the care they were able to give, and this caused moral distress. When staff were able to provide PCC, this increased experiences of job satisfaction and emotional well-being.

CONCLUSIONS

Person-centred care not only has the potential to improve the experience of care for Plwd and their carers, but can also improve the experiences of hospital staff caring for Plwd. However, without institutional-level changes, hospital staff are often unable to provide PCC even when they have the experience and knowledge to do so.

IMPLICATIONS FOR PRACTICE

Institutional-level areas for change include the following: training; performance indicators and ward cultures that prioritise psychological needs alongside physical needs; adequate staffing levels; inclusive approaches to carers; physical environments that promote familiarisation, social interaction and occupation; systems of documentation about individual needs of Plwd; and cultures of sharing knowledge across hierarchies.

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.

Which Risk Factors Causally Influence Dementia? A Systematic Review of Mendelian Randomization Studies

Background:

Numerous risk factors for dementia are well established, though the causal nature of these associations remains unclear.

Objective:

To systematically review Mendelian randomization (MR) studies investigating causal relationships between risk factors and global cognitive function or dementia.

Methods:

We searched five databases from inception to February 2017 and conducted citation searches including MR studies investigating the association between any risk factor and global cognitive function, all-cause dementia or dementia subtypes. Two reviewers independently assessed titles and abstracts, full-texts, and study quality.

Results:

We included 18 MR studies investigating education, lifestyle factors, cardiovascular factors and related biomarkers, diabetes related and other endocrine factors, and telomere length. Studies were of predominantly good quality, however eight received low ratings for sample size and statistical power. The most convincing causal evidence was found for an association of shorter telomeres with increased risk of Alzheimer’s disease (AD). Causal evidence was weaker for smoking quantity, vitamin D, homocysteine, systolic blood pressure, fasting glucose, insulin sensitivity, and high-density lipoprotein cholesterol. Well-replicated associations were not present for most exposures and we cannot fully discount survival and diagnostic bias, or the potential for pleiotropic effects.

Conclusions:

Genetic evidence supported a causal association between telomere length and AD, whereas limited evidence for other risk factors was largely inconclusive with tentative evidence for smoking quantity, vitamin D, homocysteine, and selected metabolic markers. The lack of stronger evidence for other risk factors may reflect insufficient statistical power. Larger well-designed MR studies would therefore help establish the causal status of these dementia risk factors.

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.

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.

Electronic supplementary material

The online version of this article (doi:10.1186/s12877-017-0528-y) contains supplementary material, which is available to authorized users.

Parathyroid hormone, cognitive function and dementia: a systematic review

Background

Metabolic factors are increasingly recognized to play an important role in the pathogenesis of Alzheimer’s disease and dementia. Abnormal parathyroid hormone (PTH) levels play a role in neuronal calcium dysregulation, hypoperfusion and disrupted neuronal signaling. Some studies support a significant link between PTH levels and dementia whereas others do not.

Methods

We conducted a systematic review through January 2014 to evaluate the association between PTH and parathyroid conditions, cognitive function and dementia. Eleven electronic databases and citation indexes were searched including Medline, Embase and the Cochrane Library. Hand searches of selected journals, reference lists of primary studies and reviews were also conducted along with websites of key organizations. Two reviewers independently screened titles and abstracts of identified studies. Data extraction and study quality were performed by one and checked by a second reviewer using predefined criteria. A narrative synthesis was performed due to the heterogeneity of included studies.

Results

The twenty-seven studies identified were of low and moderate quality, and challenging to synthesize due to inadequate reporting. Findings from six observational studies were mixed but suggest a link between higher serum PTH levels and increased odds of poor cognition or dementia. Two case-control studies of hypoparathyroidism provide limited evidence for a link with poorer cognitive function. Thirteen pre-post surgery studies for primary hyperparathyroidism show mixed evidence for improvements in memory though limited agreement in other cognitive domains. There was some degree of cognitive impairment and improvement postoperatively in observational studies of secondary hyperparathyroidism but no evident pattern of associations with specific cognitive domains.

Conclusions

Mixed evidence offers weak support for a link between PTH, cognition and dementia due to the paucity of high quality research in this area.