Suchergebnisse

ABSTRACT
ObjectivesDementia is a major public health concern worldwide and consequently there is an urgent need to expedite research into its causes so that preventative strategies can be sought. Given that dementia is likely to be the result of a complex interplay of many factors, large study populations are required in order to detect effects reliably. UK Biobank (UKB) is a large, population-based, prospective cohort study of over 503,000 participants aged 40-69 years when recruited between 2006 and 2010. Participant follow up is chiefly via linkage to routinely-collected health datasets such as hospital admissions, death registrations and increasingly, to primary care data. In this pilot study we sought to estimate the accuracy of using these routine data sources to identify dementia outcomes in UKB participants.
ApproachWe created a list of ICD-10 and primary care (Read version 2) dementia codes, with the intention of maximising positive predictive value (PPV) over sensitivity. We identified UKB participants who were recruited in Edinburgh and had at least one dementia code in any of the three data sources. We searched the NHS Lothian electronic medical record (EMR) for each participant and extracted all relevant letters and investigation results. Participants were excluded if no EMR entry for that patient could be found. A neurologist adjudicated on whether dementia was present or not based on the extracted case record, providing the reference standard to which the coded data were compared. The PPV was then calculated for each data source individually and combined. A subgroup analysis was performed on participants who had a dementia code across more than one dataset.
ResultsAmong 17,000 Edinburgh-based participants (median age 57 years at recruitment in 2007/8), hospital and death data were available to 2012 with primary care data for 12,000 to 2013. 46 participants had a dementia code in at least one data source. 44 of these had available EMR data. PPVs for dementia were 41/44 (93%, 95% CI 81-99) overall, 13/15 (87%, 95% CI 60-98) for hospital admissions, 2/2 (100%, 95% CI 16-100) for death registrations, 33/34 (97%, 95% CI 85-100) for primary care, and 7/7 (100%, 95% CI 59-100) for participants with codes in ≥2 datasets.
ConclusionRoutinely-collected health data may be sufficiently accurate to identify dementia outcomes in UK population-based cohorts. We plan to extend this study to longer follow-up times and other regions to increase sample size, investigate dementia subtypes and assess generalisability.

Abstract
Purpose
Diagnosing dementia, affecting over 55 million people globally, is challenging and costly, often leading to late-stage diagnoses. This study aims to develop early, accurate, and cost-effective dementia screening methods using exposome predictors and machine learning. We investigate whether low-cost exposome predictors combined with machine learning models can reliably identify individuals at risk of dementia.

Methods
We analyzed data from 500,000 UK Biobank participants, selecting 1523 diagnosed with dementia and an equal number of healthy controls, matched by age and sex. A total of 3046 participants were included: 2740 for internal validation and 306 for external validation. We used 128 low-cost exposome factors from baseline visits, imputed missing data, and assessed two predictive models: a classical logistic regression and a machine learning ensemble classifier (XGBoost). Feature importance was estimated within the predictive models.

Results
The XGBoost model outperformed the logistic regression model, achieving a mean AUC of 0.88 in external validation. We identified novel exposome factors that might be combined as potential markers for dementia, such as facial aging, the frequency of use of sun/ultraviolet light protection, and the length of mobile phone use.

Conclusions
Machine learning models utilizing exposome data can reliably identify individuals at risk of dementia, with XGBoost showing superior performance. This approach highlights the potential of low-cost, readily available exposome factors as markers for dementia. Future studies should validate these findings in diverse populations and explore the integration of additional exposome factors to enhance prediction accuracy.

Introduction Dementia Platform UK (DPUK) brings together over 50 different dementia-related cohorts. Most studies have restricted follow-up times and all are based on information from people who volunteer time and data for research. Participants are therefore often not representative of the 'wider population' and generalization of results is complicated. The Secure Anonymised Information Linkage databank (SAIL) holds long-time information on every person in Wales registered with the national health service, so generalization of study results is easier; however, data management and analysis of SAIL data is not trivial. We used data from SAIL to construct an easily accessible, well described dementia e-cohort.
Methods With some age restrictions, all Welsh residents for whom primary care data were available were included. Within SAIL, a table was created holding demographic information for every participant including follow-up times and several dementia indicators. Using validated diagnostic code lists, this table was linked to information on every dementia-related diagnostic event and several covariates and co-morbidites. SAIL-DeC can be modified according to varying study designs using annotated SQL-based scripts. Information on SAIL-DeC can easily be updated and linked to additional data on the SAIL database. Interactive visualisations effectively summarise cohort characteristics, aiding researchers to quickly determine cohort eligibility for dementia studies.
Results From 4.4 million participants in SAIL, 1.2 million met the cohort inclusion criteria, resulting in 18.8 million person-years of follow-up. Of these, 129,650 (10%) developed all-cause dementia during follow-up, with 77,978 (60%) having dementia subtype codes. Seventy-nine percent of participants who developed dementia died during follow-up. Median survival was 12.3 years for participants diagnosed with dementia when aged 50-60, 6.8 years when aged 60-70, 4.2 years when aged 70-80 and 2.4 years when aged 80-90.
Conclusions We have created a generalisable, national dementia e-cohort, aimed at facilitating epidemiological dementia research.

IntroductionResearch can often be slow to start and require duplication of effort which in lots of cases has previously been completed to generate research-ready-data-assets (RRDA). Within the UK, two programmes: Dementias Platform UK (DPUK) that brings together over 50 different dementia-related cohorts and Secure Anonymised Information Linkage (SAIL) Databank, which provides access to longitudinal population-scale person-level data for every person in Wales have tried to tackle this challenge of creation, use and management of RRDA's.
Objectives and ApproachCombining clinical, data and management expertise from DPUK and SAIL, we hoped to construct a RRDA that was easily accessible and well described for a dementia e-cohort. Welsh residents with available primary care records were included, with clinical and demographic information including follow-up times, several dementia indicators using validated diagnostic code lists, information on every dementia-related diagnostic event and several covariates and co-morbidities. SDeC was made available to researchers and can be modified according to appropriate study designs, with learning from projects used to update the SDeC to improve future uses. Interactive visualisations effectively summarise cohort characteristics, aiding researchers to quickly determine cohort eligibility for dementia studies.
ResultsSDeC contains data from 4.6 million participants in SAIL, with 1.5 million meeting cohort inclusion criteria, resulting in 24.3 million person-years of follow-up. Of these, 146,323 (10%) developed all-cause dementia during follow-up, with 90,150 (60%) having dementia subtype codes. We made this resource available to researchers who had never used SAIL before, with limited experience of population-scale routine-data, and projects have proceeded with one managing to proceed from point of initial access to submission of publication in less than 6-months.
Conclusion / ImplicationsSDeC provides a reproducible dynamic method for completing dementia research, and expediting learning and understanding of the use of these data, with further developments and maintenance planned to increase the complexity and detail available to researchers over time.

IntroductionThe rising burden of dementia is a global concern, and there is a need to study its causes, natural history and outcomes. The Secure Anonymised Information Linkage (SAIL) Databank contains anonymised, routinely-collected healthcare data for the population of Wales, UK. It has potential to be a valuable resource for dementia research owing to its size, long follow-up time and prospective collection of data during clinical care.
ObjectivesWe aimed to apply reproducible methods to create the SAIL dementia e-cohort (SAIL-DeC). We created SAIL-DeC with a view to maximising its utility for a broad range of research questions whilst minimising duplication of effort for researchers.
MethodsSAIL contains individual-level, linked primary care, hospital admission, mortality and demographic data. Data are currently available until 2018 and future updates will extend participant follow-up time. We included participants who were born between 1st January 1900 and 1st January 1958 and for whom primary care data were available. We applied algorithms consisting of International Classification of Diseases (versions 9 and 10) and Read (version 2) codes to identify participants with and without all-cause dementia and dementia subtypes. We also created derived variables for comorbidities and risk factors.
Results
From 4.4 million unique participants in SAIL, 1.2 million met the cohort inclusion criteria, resulting in 18.8 million person-years of follow-up. Of these, 129,650 (10%) developed all-cause dementia, with 77,978 (60%) having dementia subtype codes. Alzheimer's disease was the most common subtype diagnosis (62%). Among the dementia cases, the median duration of observation time was 14 years.
ConclusionsWe have created a generalisable, national dementia e-cohort, aimed at facilitating epidemiological dementia research.

AbstractObjectivesThere is evidence that loneliness is detrimental to the subjective well-being of older adults. However, little is known on this topic for the cohort of those in advanced age (80 years or older), which today is the fastest-growing age group in the New Zealand population. We examined the relationships between loneliness and selected subjective well-being outcomes over 5 years.MethodsWe used a regional, bicultural sample of those in advanced age from 2010 to 2015 (Life and Living in Advanced Age: a Cohort Study in New Zealand). The first wave enrolled 937 people (92% of whom were living in the community): 421 Māori (Indigenous New Zealanders aged 80–90 years) and 516 non-Māori aged 85 years. We applied standard regression techniques to baseline data and mixed-effects models to longitudinal data, while adjusting for sociodemographic factors.ResultsFor both Māori and non-Māori, strong negative associations between loneliness and subjective well-being were found at baseline. In longitudinal analyses, we found that loneliness was negatively associated with life satisfaction as well as with mental health-related quality of life.DiscussionOur findings of adverse impacts on subjective well-being corroborate other evidence, highlighting loneliness as a prime candidate for intervention—appropriate to cultural context—to improve well-being for adults in advanced age.

Societal Impact Statement Plant and fungal specimens provide the auditable evidence that a particular organism occurred at a particular place, and at a particular point in time, verifying past occurrence and distribution. They also document the aspects of human exploration and culture. Collectively specimens form a global asset with significant potential for new uses to help address societal and environmental challenges. Collections also serve as a platform to engage and educate a broad range of stakeholders from the academic to the public, strengthening engagement and understanding of plant and fungal diversity—the basis of life on Earth. Summary We provide a global review of the current state of plant and fungal collections including herbaria and fungaria, botanic gardens, fungal culture collections, and biobanks. The review focuses on the numbers of collections, major taxonomic group and species level coverage, geographical representation and the extent to which the data from collections are digitally accessible. We identify the major gaps in these collections and in digital data. We also consider what collection types need to be further developed to support research, such as environmental DNA and cryopreservation of desiccation-sensitive seeds. Around 31% of vascular plant species are represented in botanic gardens, and 17% of known fungal species are held in culture collections, both these living collections showing a bias toward northern temperate taxa. Only 21% of preserved collections are available via the Global Biodiversity Information Facility (GBIF) with Asia, central and north Africa and Amazonia being relatively under-represented. Supporting long-term collection facilities in biodiverse areas should be considered by governmental and international aid agencies, in addition to short-term project funding. Institutions should consider how best to speed up digitization of collections and to disseminate all data via aggregators such as GBIF, which will greatly facilitate use, research, and community curation to improve quality. There needs to be greater alignment between biodiversity informatics initiatives and standards to allow more comprehensive analysis of collections data and to facilitate linkage of extended information, facilitating broader use. Much can be achieved with greater coordination through existing initiatives and strengthening relationships with users. ; This is an open access article, available to all readers online, published under a creative commons licensing (https://creativecommons.org/licenses/by/4.0/).

Societal Impact Statement Plant and fungal specimens provide the auditable evidence that a particular organism occurred at a particular place, and at a particular point in time, verifying past occurrence and distribution. They also document the aspects of human exploration and culture. Collectively specimens form a global asset with significant potential for new uses to help address societal and environmental challenges. Collections also serve as a platform to engage and educate a broad range of stakeholders from the academic to the public, strengthening engagement and understanding of plant and fungal diversity—the basis of life on Earth. Summary We provide a global review of the current state of plant and fungal collections including herbaria and fungaria, botanic gardens, fungal culture collections, and biobanks. The review focuses on the numbers of collections, major taxonomic group and species level coverage, geographical representation and the extent to which the data from collections are digitally accessible. We identify the major gaps in these collections and in digital data. We also consider what collection types need to be further developed to support research, such as environmental DNA and cryopreservation of desiccation‐sensitive seeds. Around 31% of vascular plant species are represented in botanic gardens, and 17% of known fungal species are held in culture collections, both these living collections showing a bias toward northern temperate taxa. Only 21% of preserved collections are available via the Global Biodiversity Information Facility (GBIF) with Asia, central and north Africa and Amazonia being relatively under‐represented. Supporting long‐term collection facilities in biodiverse areas should be considered by governmental and international aid agencies, in addition to short‐term project funding. Institutions should consider how best to speed up digitization of collections and to disseminate all data via aggregators such as GBIF, which will greatly facilitate use, research, and community ...

The Dementias Platform UK Data Portal is a data repository facilitating access to data for 3 370 929 individuals in 42 cohorts. The Data Portal is an end-to-end data management solution providing a secure, fully auditable, remote access environment for the analysis of cohort data. All projects utilising the data are by default collaborations with the cohort research teams generating the data. The Data Portal uses UK Secure eResearch Platform infrastructure to provide three core utilities: data discovery, access, and analysis. These are delivered using a 7 layered architecture comprising: data ingestion, data curation, platform interoperability, data discovery, access brokerage, data analysis and knowledge preservation. Automated, streamlined, and standardised procedures reduce the administrative burden for all stakeholders, particularly for requests involving multiple independent datasets, where a single request may be forwarded to multiple data controllers. Researchers are provided with their own secure 'lab' using VMware which is accessed using two factor authentication. Over the last 2 years, 160 project proposals involving 579 individual cohort data access requests were received. These were received from 268 applicants spanning 72 institutions (56 academic, 13 commercial, 3 government) in 16 countries with 84 requests involving multiple cohorts. Projects are varied including multi-modal, machine learning, and Mendelian randomisation analyses. Data access is usually free at point of use although a small number of cohorts require a data access fee.

The Dementias Platform UK Data Portal is a data repository facilitating access to data for 3 370 929 individuals in 42 cohorts. The Data Portal is an end-to-end data management solution providing a secure, fully auditable, remote access environment for the analysis of cohort data. All projects utilising the data are by default collaborations with the cohort research teams generating the data. The Data Portal uses UK Secure eResearch Platform infrastructure to provide three core utilities: data discovery, access, and analysis. These are delivered using a 7 layered architecture comprising: data ingestion, data curation, platform interoperability, data discovery, access brokerage, data analysis and knowledge preservation. Automated, streamlined, and standardised procedures reduce the administrative burden for all stakeholders, particularly for requests involving multiple independent datasets, where a single request may be forwarded to multiple data controllers. Researchers are provided with their own secure 'lab' using VMware which is accessed using two factor authentication. Over the last 2 years, 160 project proposals involving 579 individual cohort data access requests were received. These were received from 268 applicants spanning 72 institutions (56 academic, 13 commercial, 3 government) in 16 countries with 84 requests involving multiple cohorts. Projects are varied including multi-modal, machine learning, and Mendelian randomisation analyses. Data access is usually free at point of use although a small number of cohorts require a data access fee.

The Dementias Platform UK Data Portal is a data repository facilitating access to data for 3 370 929 individuals in 42 cohorts. The Data Portal is an end-to-end data management solution providing a secure, fully auditable, remote access environment for the analysis of cohort data. All projects utilising the data are by default collaborations with the cohort research teams generating the data. The Data Portal uses UK Secure eResearch Platform infrastructure to provide three core utilities: data discovery, access, and analysis. These are delivered using a 7 layered architecture comprising: data ingestion, data curation, platform interoperability, data discovery, access brokerage, data analysis and knowledge preservation. Automated, streamlined, and standardised procedures reduce the administrative burden for all stakeholders, particularly for requests involving multiple independent datasets, where a single request may be forwarded to multiple data controllers. Researchers are provided with their own secure 'lab' using VMware which is accessed using two factor authentication. Over the last 2 years, 160 project proposals involving 579 individual cohort data access requests were received. These were received from 268 applicants spanning 72 institutions (56 academic, 13 commercial, 3 government) in 16 countries with 84 requests involving multiple cohorts. Projects are varied including multi-modal, machine learning, and Mendelian randomisation analyses. Data access is usually free at point of use although a small number of cohorts require a data access fee.