Dementias Platform UK Research Articles

Developing a Framework for Safe AI Model Development on Sensitive Healthcare Data

The Dementias Platform UK (DPUK) Data Portal holds over 60 cohort datasets from over 3 million participants with a range of multi-modal data including neuroimaging and genomics. This has meant we have seen an increasing interest in the development of AI models with the potential for clinical implementation. However, this presents a unique challenge to disclosure control when it comes to assessing these AI models for release due to the risk of attacks such as membership inference, model inversion or even just vulnerabilities in the models like overfitting. This is why we hosted a series of workshops bringing together members of the public, expert researchers, and data owners across the UK to build a framework for allowing the safe development and release of AI models trained on sensitive healthcare data. From this, we have put together recommendations and guidelines for the use of privacy-preserving techniques in AI models to protect patient privacy, and to allow the safe deployment of these models outside of trusted research environments. We also identified the unique challenges to privacy and implementation of privacy-preserving techniques in AI models regarding the use of complex data such as neuroimaging and genomics. This framework has created a path forward for supporting safe AI model development which takes into consideration rapidly evolving ethical, legal and privacy considerations.

Modelling the joint accuracy of two diagnostic tests using evidence synthesis methods incorporating linked electronic health records

Background and ObjectivesTo compare the accuracy of different diagnostic pathways comprising of multiple diagnostic tests, statistical methods that evaluate combined or sequential test accuracy are required. This can only be achieved through joint synthesis of diagnostic accuracy data on all tests of interest, using models that account for dependencies between multiple tests. The aim of this research is to develop joint models to assess the diagnostic accuracy of two tests, using real-world evidence to inform within-study dependencies between tests. ApproachUsing a case study in Alzheimer’s disease dementia (ADD), we will develop Bayesian meta-analysis models to evaluate the combined diagnostic accuracy of two tests. A novel application of trivariate copulas will be used to capture within-study dependencies between two tests assessed in the same patients. Individual participant data from Dementias Platform UK (DPUK) Data Portal – a repository of linked electronic health record data optimised for dementia research – will be used to estimate dependencies between diagnostic tests for ADD. ResultsThe DPUK Data Portal includes records of over 3 million people from 51 long-term cohort studies, including individual-level test results for ADD verified against confirmed diagnoses. Analysis will focus on modelling the diagnostic accuracy of different testing strategies for ADD, combining cognitive function, imaging, and biomarker tests. Conclusions and ImplicationsResults of this study have the potential to inform healthcare decision-making regarding optimal diagnostic pathways by enabling the comparison of different testing strategies. The incorporation of real-world data from the DPUK Data Portal makes better use of the available evidence.

Dementias Platform UK: Bringing genetics into life.

The Dementias Platform UK (DPUK) Data Portal is a data repository bringing together a wide range of cohorts. Neurodegenerative dementias are a group of diseases with highly heterogeneous pathology and an overlapping genetic component that is poorly understood. The DPUK collection of independent cohorts can facilitate research in neurodegeneration by combining their genetic and phenotypic data. For genetic data processing, pipelines were generated to perform quality control analysis, genetic imputation, and polygenic risk score (PRS) derivation with six genome-wide association studies of neurodegenerative diseases. Pipelines were applied to five cohorts. The data processing pipelines, research-ready imputed genetic data, and PRS scores are now available on the DPUK platform and can be accessed upon request though the DPUK application process. Harmonizing genome-wide data for multiple datasets increases scientific opportunity and allows the wider research community to access and process data at scale and pace.

The usability and reliability of a smartphone application for monitoring future dementia risk in ageing UK adults.

The rising number of dementia diagnoses and imminent adoption of disease-modifying treatments necessitate innovative approaches to identify individuals at risk, monitor disease course and intervene non-pharmacologically earlier in the disease course. Digital assessments of dementia risk and cognitive function have the potential to outperform traditional in-person assessments in terms of their affordability, accuracy and longitudinal tracking abilities. However, their accessibility and reliability in older adults is unclear. To evaluate the usability and reliability of a smartphone assessment of lifestyle and cognitive factors relevant to dementia risk in a group of UK-based older adults. Cognitively healthy adults (n = 756) recruited through the Dementias Platform UK Great Minds volunteer register completed three assessments of cognitive function and dementia risk over a 3-month period and provided usability feedback on the Five Lives smartphone application (app). We evaluated cognitive test scores for age, gender and higher education effects, normality distributions, test-retest reliability and their relationship with participants' lifestyle dementia risk factors. Participants found the app 'easy to use', 'quick to complete' and 'enjoyable'. The cognitive tests showed normal or near-to-normal distributions, variable test-retest reliabilities and age-related effects. Only tests of verbal ability showed gender and education effects. The cognitive tests did not correlate with lifestyle dementia risk scores. The Five Lives assessment demonstrates high usability and reliability among older adults. These findings highlight the potential of digital assessments in dementia research and clinical practice, enabling improved accessibility and better monitoring of cognitive health on a larger scale than traditional in-person assessments.

Investigating the prevalence of cognitive impairment and dementia in the Northern Ireland Cohort for the Longitudinal Study of Ageing (NICOLA): the Harmonised Cognitive Assessment Protocol (HCAP) cross-sectional substudy

IntroductionThe Northern Ireland Cohort for the Longitudinal Study of Ageing (NICOLA) study is the largest study of ageing in Northern Ireland (NI). The Harmonised Cognitive Assessment Protocol (HCAP) is a...

Dementias Platform UK: Bringing genetics into life

AbstractBackgroundDPUK is a data repository bringing together a wide range of population and clinical cohorts from the UK, Europe and South Korea. It enables data discovery, variable selection, and data access for multi‐modal, multi‐cohort analysis in a shared and secure environment. Neurodegenerative dementias are group of diseases with highly heterogeneous pathology and with an overlapping genetic component that is poorly understood. Combing genetic data from different studies is important due to the increase in power this provides, but this presents an additional challenge due to differences in genotyping arrays and lack of overlapping variants.MethodWe have curated and harmonized genome‐wide data from 6 studies (Brains for Dementia Research, EPIC Norfolk, Generation Scotland, Airwave‐chip 1, Airwave‐chip 2, MRC National Survey of Health Development (NSHD)) within the DPUK platform. We created a pipeline that performs rigorous quality‐control (QC) analysis and imputes genetic variance with the 1000 Genome reference panel using the minimac algorithm. Further, standard pruning and thresholding Polygenic Risk Score (PRS) have been generated with 5 summary statistics related to neurodegenerative diseases (clinical AD, clinical/proxy AD, FTD, ALS, PD) for all 6 studies separately and for the combined dataset.ResultResearch‐ready imputed genetic data that have undergone QC and the PRS, based upon the latest neurodegenerative GWAS, are available for the 6 cohorts separately, and for the combined dataset of 60,670 individuals.ConclusionPreparing multiple datasets to a common standard for research‐readiness increases scientific opportunity and allows the wider research community to access and process data at scale and pace.

Oxford brain health clinic: protocol and research database

IntroductionDespite major advances in the field of neuroscience over the last three decades, the quality of assessments available to patients with memory problems in later life has barely changed. At...

Northern Ireland Cohort for the Longitudinal Study of Ageing (NICOLA): health assessment protocol, participant profile and patterns of participation

BackgroundThe Northern Ireland Cohort for the Longitudinal Study of Ageing (NICOLA) is a prospective, longitudinal study of a representative cohort of older adults living in Northern Ireland, United Kingdom. Its aim is to explore the social, behavioural, economic and biological factors of ageing and how these factors change as people age. The study has been designed to maximize comparability with other international studies of ageing thereby facilitating cross-country comparisons. This paper provides an overview of the design and methodology of the health assessment which was carried out as part of Wave 1.MethodsThree thousand, six hundred and fifty five community dwelling adults, aged 50 years and over participated in the health assessment as part of Wave 1 of NICOLA. The health assessment included a battery of measurements across various domains that addressed key indicators of ageing namely: physical function, vision and hearing, cognitive function, and cardiovascular health. This manuscript describes the scientific rationale for the choice of assessments, provides an overview of the core objective measures carried out in the health assessment and describes the differences in characteristics of participants who took part in the health assessment compared to those who did not take part.ResultsThe manuscript highlights the importance of incorporating objective measures of health in population based studies as a means of complementing subjective measures and as a way to advance our understanding of the ageing process. The findings contextualize NICOLA as a data resource within Dementias Platform UK (DPUK), the Gateway to Global Ageing (G2G) and other existing networks of population based longitudinal studies of ageing.ConclusionThis manuscript can help inform design considerations for other population based studies of ageing and facilitate cross-country comparative analysis of key life-course factors affecting healthy ageing such as educational attainment, diet, the accumulation of chronic conditions (including Alzheimer’s disease, dementia and cardiovascular disease) as well as welfare and retirement policies.

New Therapeutics in Alzheimer’s Disease Longitudinal Cohort study (NTAD): study protocol

IntroductionWith the pressing need to develop treatments that slow or stop the progression of Alzheimer’s disease, new tools are needed to reduce clinical trial duration and validate new targets for...

Can we re‐purpose drugs from the cardiology medicine cabinet? Should we look in the rheumatology, metabolic and hematology cabinets as well?

AbstractBackgroundDespite substantial investment, the dementia drug development pipeline has failed to produce a convincing new pharmacological treatment. This situation is in stark contrast to drug development in oncology, metabolic and cardiovascular medicine, where many novel agents are entering phase III trials or clinical practice. Some of the commonly prescribed medications for brain disorders were originally developed for another therapeutic indication (memantine and tPA are examples). In this session we will consider whether re‐purposing existing or novel drugs from other disease areas could offer a treatment for dementia?MethodThe ultimate test of a new medication is the randomised controlled trial. Contemporary trials are costly. For drug repurposing we can exploit available data from trials and registries to inform the choice of candidate drugs and ‘de‐risk’ the development process. There are various approaches, and we will consider: expert consensus; systematic review and meta‐analysis; individual participant level evidence synthesis; genome wide association studies; drug wide association studies. Secondary analysis of existing trial and healthcare data can also inform the design, sample size and conduct of a repurposing study. As part of the Dementias Platform UK (DPUK) portfolio, we are utilising all these methods with the anticipation of selecting the most promising candidates to take forward to clinical trials.ResultThere are already good examples where existing data have informed or are informing drug repurposing studies. These include non‐steroidal and other anti‐inflammatory drugs (e.g. cilostazol); antithrombotic agents (e.g. dabigatran); antihypertensive or vasodilator medications (e.g. losartan, tadalafil); novel drugs developed for heart failure and diabetes mellitus (e.g. GLP1 agonists, such as liraglutide). Emerging data are already suggesting plausible candidates, while also giving new insights into the pathogenesis that underlies cognitive decline. We will share progress made within the DPUK Vascular group and offer a roadmap for future repurposing research.ConclusionThe search for an effective dementia intervention continues. It is possible that we already have useful medications that we can ‘borrow’ from other clinical fields. The increasing number of medications coupled with access to large volume, robust data‐sets creates an ideal landscape for selecting candidate drugs for repurposing.

Magnetoencephalography insights to dementia and drug intervention

AbstractBackgroundMagnetoencephalography (MEG) is a promising tool for experimental medicine in dementia, and a core technology for the Dementias Platform UK. It quantifies in vivo human network and synaptic physiology, with high‐dimensional data at a millisecond time‐scale. Its proven sensitivity to neurodegenerative disease has applications in diagnostics and early detection, mechanistic studies of pathogenesis, and biomarkers to accelerate clinical trials and evidence target engagement. Here, we build on recent work with Serotonin and GABA‐ergic modulation, to illustrate MEG insights into the neurophysiological effects of the NMDA receptor antagonist Memantine. We use Frontotemporal dementia and Progressive supranuclear palsy (bvFTD, PSP) as demonstrator conditions in view of their focal glutamatergic deficits.Method24 Patients with bvFTD and PSP, and 20 healthy controls underwent magnetoencephalography, 7T MRI spectroscopy and a battery of cognitive tests in a double‐blind placebo‐controlled crossover design, using Memantine 10mg/Placebo. We test the relationship between cortical oscillations, baseline glutamate concentration and response to pharmacological intervention.ResultNeurophysiological activity was impaired in patients, with diminished low frequency oscillations and disrupted cortical connectivity. These abnormalities correlated with reduced baseline glutamate concentration. Memantine enhanced the neuronal response, particularly in prefrontal regions.ConclusionMEG provides insights into targetable pathophysiological effects of neurodegeneration and markers for treatment studies. It is sensitive to disease and individual differences in pathophysiology, reliable, safe, well‐tolerated and sufficiently scalable for early phase trials. MEG evidence of selective neuronal dysfunction and network reorganisation can couple with microcircuit models for synaptic assays, or be used directly as a readout for interventional studies. MEG is ideally suited to support both academic and pharma initiatives, with quantitative tools for early phase clinical trials, illustrated here with Memantine, but applicable to other compounds.

Using item response theory (IRT) to improve the efficiency of the Simple Clinical Colitis Activity Index (SCCAI) for patients with ulcerative colitis

BackgroundThe SCCAI was designed to facilitate assessment of disease activity in ulcerative colitis (UC). We aimed to interrogate the metric properties of individual items of the SCCAI using item response theory (IRT) analysis, to simplify and improve its performance.MethodsThe original 9-item SCCAI was collected through TrueColours, a real-time software platform which allows remote entry and monitoring of patients with UC. Data were securely uploaded onto Dementias Platform UK Data Portal, where they were analysed in Stata 16.1 SE. A 2-parameter (2-PL) logistic IRT model was estimated to evaluate each item of the SCCAI for its informativeness (discrimination). A revised scale was generated and re-assessed following systematic removal of items.ResultsSCCAI data for 516 UC patients (41 years, SD = 15) treated in Oxford were examined. After initial item deletion (Erythema nodosum, Pyoderma gangrenosum), a 7-item scale was estimated. Discrimination values (information) ranged from 0.41 to 2.52 indicating selected item inefficiency with three items < 1.70 which is a suggested discriminatory value for optimal efficiency. Systematic item deletions found that a 4-item scale (bowel frequency day; bowel frequency nocturnal; urgency to defaecation; rectal bleeding) was more informative and discriminatory of trait severity (discrimination values of 1.50 to 2.78). The 4-item scale possesses higher scalability and unidimensionality, suggesting that the responses to items are either direct endorsement (patient selection by symptom) or non-endorsement of the trait (disease activity).ConclusionReduction of the SCCAI from the original 9-item scale to a 4-item scale provides optimum trait information that will minimise response burden. This new 4-item scale needs validation against other measures of disease activity such as faecal calprotectin, endoscopy and histopathology.

DPUK Cohort Explorer: An Interactive Visualisation Tool

IntroductionThe Dementias Platform UK (DPUK) Cohort Explorer is an interactive, online visualisation tool that allows users to explore data for a number of DPUK cohorts. Over 30 variables across cohorts have been harmonised, including information on demographics, lifestyle, cognition, health, and genetic biomarkers.&#x0D; Objectives and ApproachThe tool has been developed to complement existing DPUK cohort metadata to provide a visual representation of participant numbers and field-level information for a selection of cohorts. This enables users to determine a cohort’s eligibility before applying for access to a cohort’s data, and aid in shaping potential hypotheses. Developed using Microsoft PowerBI, the Explorer hosts a subset of the cohort’s baseline, harmonised data, allowing a user to interrogate the visualisations of the uploaded data in a secure manner on the DPUK Data Portal website. Visualisations are linked so that participant numbers and distributions can be explored interactively.&#x0D; ResultsThis approach allows the user to explore the harmonised data across a number of cohorts simultaneously whilst setting and adjusting filters that are of interest to the user’s search criteria. This provides a better understanding of the real-world data and enables the user to determine the feasibility of each cohort for potential studies, whilst facilitating meaningful comparisons across cohorts. The tool currently visualises five DPUK cohorts with a total of 82,391 participants, however it is being incrementally developed with more cohorts being added continually.&#x0D; Conclusion / ImplicationsBy combing an easy-to-use, interactive dashboard with harmonised sets of real-world data, the tool allows the user to explore, interrogate and better understand field-level information in a secure manner with zero data transfer. This provides more insight for the user when applying for access to a cohort dataset using the DPUK Data Portal and may help the user to make more informed decisions and/or hypotheses.

Creating A Research Ready Data Asset and Empowering Dynamic and Efficient Research: The Sail Dementia E-Cohort (SDEC)

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.&#x0D; 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.&#x0D; 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.&#x0D; 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.

Dementias Platform UK (DPUK): Facilitating cross‐cohort analysis in a digital age

AbstractBackgroundDementias Platform UK (DPUK) is a £53M public‐private partnership established by the MRC to provide access to large‐scale cohort data and accelerate the research and discovery of new treatments for dementia. The DPUK Data Portal facilitates multi‐modal remote data access to 3.4M individuals from 42 cohorts using a secure, robust, persistent, and fully auditable data repository. To facilitate the analysis of multiple independent datasets, DPUK curates cohort data to the C‐Surv data model. This model standardises data structure, variable naming and value labelling conventions to reduce data pre‐processing times and reduce unwanted variation. C‐Surv also enables the harmonisation of datasets for analysis feasibility testing. We present the C‐Surv ontology and demonstrate its impact on data discovery and knowledge management for multi‐cohort, multi‐modal analysis in dementia research.Method Ontology: The C‐Surv ontology has 22 level‐1 and 132 level‐2 categories, written to be machine‐readable for mapping to other ontologies and is optimised for cohort (survey) data. C‐Surv accommodates heterogeneous data‐types across cohorts. Higher order data (imaging, genetics, devices) are pre‐processed prior to curation. Researchers may request access to native or curated data.Results Standardisation: C‐Surv standardisation process: standardised naming structure of variables – maximum coding structure of 12 uppercase alphanumeric characters selected, with 5 additional characters for the imaging, linkage, genomic categories and accelerometer data. An intuitive coding structure is adopted whereby syllable‐based acronyms, word fragments, abbreviations and minimal numeric characters are utilised. Harmonisation: C‐Surv provides a harmonised dataset of 30 common data elements (variables) which are widely used in neurodegenerative and bio‐epidemiological research, and can be visualised in the DPUK Cohort Explorer. The C‐Surv standard data structure allows efficient data discovery and data selection. Building on the DPUK Cohort Matrix and Cohort Directory, the C‐Surv standard data structure provides a framework for the development of ergonomic data discovery and data selection tools.ConclusionThe C‐Surv ontology is a data model optimised for analysis of epidemiologic survey data. It enables analysts to conduct multi‐cohort multi‐modal analyses rapidly; offering reduced administrative load to researchers and data managers. It also reduces artifactual variation in data due to non‐standard data pre‐processing.

The DEMON Network: The first UK network for deep dementia phenotyping

AbstractBackgroundCollaborative interdisciplinary research will be key to advances in dementia prevention, identification and treatment. We established the DEMON Network to revolutionise dementia research by bringing innovators together to harness the power of data science and artificial intelligence with funding from Alzheimer’s Research UK and the Alan Turing Institute. Membership is free, and open to data scientists, dementia researchers, clinicians and industry partners. Benefits include a framework for national collaboration, research initiatives and networking events.MethodThe DEMON Network Steering Committee includes Profs David Llewellyn and Richard Everson (both University of Exeter and Alan Turing Institute), Dr Carol Routledge (Alzheimer’s Research UK) and Dr Sarah Bauermeister (University of Oxford). Membership is coordinated using an online marketing platform. Recruitment is ongoing and has included a press release, a National Institute of Health Research Dementia Researcher podcast, Twitter (@DEMONNetworkUK) and partner organisations such as Dementias Platform UK. A website was developed to feature Network opportunities and member profiles (www.demondementia.com). A Clinical Advisory Panel was established to provide clinical support, and recruitment to a Public and Patient Involvement (PPI) Group is underway.ResultMembership on 20th January 2020 comprised 180 individuals including 19 Clinical Advisory Panellists. 87 members (48%) have expertise in data science, 120 (67%) in dementia research, 48 (27%) clinical, and 16 (9%) in industry. 141 members (78%) are interested in networking events, 136 (76%) in seminars and workshops, 117 (65%) in skills exchange, and 101 (56%) in training and career development opportunities. 17 Regional Leads were recruited through Alzheimer’s Research UK Network Centres to support regional activities, and 4 National Theme Leads (Data Science, Clinical and Early Career Development) were recruited to facilitate strategy development in priority areas. Initial activity includes three datathons run in collaboration with Medical Research Council Dementias Platform UK (MRC DPUK).ConclusionThe DEMON Network has successfully launched and activities are underway reflecting member preferences. A National Strategy Workshop will be convened in 2020. Those interested can join the Network via the website.

Dementias Platform UK Clinical Studies and Great Minds Register: protocol of a targeted brain health studies recontact database

IntroductionThe case for de-risking neurodegenerative research and development through highly informative experimental medicine studies early in the disease process is strong. Such studies depend on the availability of genetic as...

Longitudinal Changes in Diet Quality over Adulthood and Physical Function in Older Life: Findings from a British Birth Cohort

Abstract Objectives A healthy diet has been linked to better physical function in older adults but evidence on the role of cumulative dietary exposure is scarce. Sex differences in the relationship are also underexplored. The objective was to explore the longitudinal association of diet quality in adulthood (from 36–64 y), in line with dietary guidelines, and walking speed as an objective measure of later life physical function. Methods Study sample was derived from the Insight 46 (n = 502), a neuroscience sub-study of the longitudinal National Survey of Health and Development, UK. Diet was assessed four times, at age 36 y, 43 y, 53 y and 60–64 y using five-day food diaries, from which the Healthy Eating Index-2015 (HEI) was calculated (0 - low quality, 100 - high quality). Walking speed was estimated in a 10-meter walk test at self-selected pace, using an inertial measurement unit at age 69–71 y. Linear mixed models with random effects and group-based trajectory modeling were used to assess individual and group changes in HEI in adulthood. Multivariable linear regression models with sex as modifier, controlling for multiple characteristics assessed the association between adulthood HEI trajectories and walking speed at 71 y. The final sample was 226 women and 230 men. Results Men and women improved diet quality linearly in adulthood (β: 0.6 HEI/y, 95% CI: 0.5, 0.7). Three linear HEI trajectories were identified (21% “Low” βo: 33, β: 3.6; 59% “Medium” βο: 38, β: 6.1; 20% “High” βο: 52, β: 5.5, P &amp;lt; 0.001). Women had faster increase rates, higher HEI at each age (β: 4 HEI points, 95% CI: 3, 6) and were more likely to belong to the “High” HEI trajectory than men (β: 1.97, P &amp;lt; 0.001). There was no overall association but in women a 10 point increase in predicted HEI trajectory and membership in the “High” vs “Medium”-“Low” HEI trajectories were associated with faster walking speed (β: 0.04 m/s, 95% CI: 0.01, 0.08, β: 0.06 m/s, 95% CI: 0.01, 0.11, respectively). Association was attenuated in multivariable models. No association was observed in men. Conclusions In women, higher diet quality in adulthood was associated with faster walking speed in later life. Achieving and maintaining high diet quality in adulthood is likely of importance to yield benefits in physical function. Funding Sources Medical Research Council UK Alzheimer's Research UK Dementias Platform UK Wolfson Foundation UK CLOSER UK.

The Dementias Platform UK (DPUK) Data Portal

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.

Associations between blood pressure across adulthood and late-life brain structure and pathology in the neuroscience substudy of the 1946 British birth cohort (Insight 46): an epidemiological study

SummaryBackgroundMidlife hypertension confers increased risk for cognitive impairment in late life. The sensitive period for risk exposure and extent that risk is mediated through amyloid or vascular-related mechanisms are poorly understood. We aimed to identify if, and when, blood pressure or change in blood pressure during adulthood were associated with late-life brain structure, pathology, and cognition.MethodsParticipants were from Insight 46, a neuroscience substudy of the ongoing longitudinal Medical Research Council National Survey of Health and Development, a birth cohort that initially comprised 5362 individuals born throughout mainland Britain in one week in 1946. Participants aged 69–71 years received T1 and FLAIR volumetric MRI, florbetapir amyloid-PET imaging, and cognitive assessment at University College London (London, UK); all participants were dementia-free. Blood pressure measurements had been collected at ages 36, 43, 53, 60–64, and 69 years. We also calculated blood pressure change variables between ages. Primary outcome measures were white matter hyperintensity volume (WMHV) quantified from multimodal MRI using an automated method, amyloid-β positivity or negativity using a standardised uptake value ratio approach, whole-brain and hippocampal volumes quantified from 3D-T1 MRI, and a composite cognitive score—the Preclinical Alzheimer Cognitive Composite (PACC). We investigated associations between blood pressure and blood pressure changes at and between 36, 43, 53, 60–64, and 69 years of age with WMHV using generalised linear models with a gamma distribution and log link function, amyloid-β status using logistic regression, whole-brain volume and hippocampal volumes using linear regression, and PACC score using linear regression, with adjustment for potential confounders.FindingsBetween May 28, 2015, and Jan 10, 2018, 502 individuals were assessed as part of Insight 46. 465 participants (238 [51%] men; mean age 70·7 years [SD 0·7]; 83 [18%] amyloid-β-positive) were included in imaging analyses. Higher systolic blood pressure (SBP) and diastolic blood pressure (DBP) at age 53 years and greater increases in SBP and DBP between 43 and 53 years were positively associated with WMHV at 69–71 years of age (increase in mean WMHV per 10 mm Hg greater SBP 7%, 95% CI 1–14, p=0·024; increase in mean WMHV per 10 mm Hg greater DBP 15%, 4–27, p=0·0057; increase in mean WMHV per one SD change in SBP 15%, 3–29, p=0·012; increase in mean WMHV per 1 SD change in DBP 15%, 3–30, p=0·017). Higher DBP at 43 years of age was associated with smaller whole-brain volume at 69–71 years of age (−6·9 mL per 10 mm Hg greater DBP, −11·9 to −1·9, p=0·0068), as were greater increases in DBP between 36 and 43 years of age (−6·5 mL per 1 SD change, −11·1 to −1·9, p=0·0054). Greater increases in SBP between 36 and 43 years of age were associated with smaller hippocampal volumes at 69–71 years of age (−0·03 mL per 1 SD change, −0·06 to −0·001, p=0·043). Neither absolute blood pressure nor change in blood pressure predicted amyloid-β status or PACC score at 69–71 years of age.InterpretationHigh and increasing blood pressure from early adulthood into midlife seems to be associated with increased WMHV and smaller brain volumes at 69–71 years of age. We found no evidence that blood pressure affected cognition or cerebral amyloid-β load at this age. Blood pressure monitoring and interventions might need to start around 40 years of age to maximise late-life brain health.FundingAlzheimer's Research UK, Medical Research Council, Dementias Platform UK, Wellcome Trust, Brain Research UK, Wolfson Foundation, Weston Brain Institute, Avid Radiopharmaceuticals.