The Dementias Platform UK (DPUK) Data Portal

Bauermeister, Sarah; Orton, Chris; Thompson, Simon; Barker, Roger A.; Bauermeister, Joshua; Ben‐Shlomo, Yoav; Brayne, Carol; Burn, David J.; Campbell, Archie; Calvin, Catherine; Chandran, Siddharthan; Chaturvedi, Nishi; Chêne, Geneviève; Chessell, Iain P.; Corbett, Anne; Davis, Daniel; Denis, Mike; Dufouil, Carole; Elliott, Paul; Fox, Nick C.; Hill, Derek L. G.; Hofer, Scott M.; Hu, Michèle; Jindra, Christoph; Kee, Frank; Kim, Chi Hun; Kim, Chang-Soo; Kivimäki, Mika; Koychev, Ivan; Lawson, Rachael A.; Linden, Gerard J.; Lyons, Ronan A; Mackay, Clare E.; Matthews, Paul M.; McGuiness, Bernadette; Middleton, Lefkos T.; Moody, Catherine; Moore, Katrina; Na, Duk L.; O’Brien, John; Ourselin, Sébastien; Paranjothy, Shantini; Park, Sun-Hee; Porteous, David J.; Richards, Marcus; Ritchie, Craig W.; Rohrer, Jonathan D.; Rossor, Martin N.; Rowe, James B.; Scahill, Rachael I.; Schnier, Christian; Schott, Jonathan M.; Seo, Sang Won; South, Matthew; Steptoe, Matthew; Tabrizi, Sarah J.; Tales, Andrea; Tillin, Therese; Timpson, Nicholas J.; Toga, Arthur W.; Visser, Pieter Jelle; Wade‐Martins, Richard; Wilkinson, Tim; Williams, Julie; Wong, Andrew; Gallacher, John

doi:10.1007/s10654-020-00633-4

Cited by 53 publications

(34 citation statements)

References 31 publications

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“…Dementias Platform UK (DPUK) is a public–private partnership funded by the Medical Research Council that aims to accelerate dementia research by bringing together longitudinal phenotypic as well as genotypic information from over 40 cohort studies. 4 Such longitudinal data are critical for the identification of participants at high risk for dementia while still in preclinical or prodromal stages. 5 Thus, DPUK is in a unique position to establish the infrastructure to offer highly characterised individuals an opportunity to consent to targeted experimental studies and clinical trials.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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 well as high-granularity, longitudinal, phenotypic data in healthy ageing individuals who can be recruited into early phase trials on the basis of their perceived dementia risk. Until now the creation of such research infrastructure has been hampered by the lack of expense and time required to gather the rich longitudinal data needed for adequate risk stratification. Dementias Platform UK (DPUK) is a public–private partnership that brings together data from over 40 cohorts in a standardised framework, which represent an until now unavailable opportunity to create such a resource through a streamlined brain health recontact platform based on existing cohorts, as well as prospectively collected data.Methods and analysisThe DPUK recontact platform consists of an opt-in (Great Minds, GM) and an opt-out component (Clinical Studies Register, CSR). GM requires invited DPUK cohort participants to consent to targeted recontact at the GM website and then to provide self-reported demographic and medical history information relevant to recruitment into clinical studies. Participants complete prospective browser-based and smartphone-based cognitive tests and are given the option for remote genetic and actigraphy testing. The GM data are linked to the retrospective DPUK cohort dataset, including genotypic and longitudinal phenotypic data. The CSR is a solution for cohorts explicitly allowing targeted recontact. Approved studies provide prescreening criteria on the basis of the CSR/GM dataset, and individuals meeting these criteria are offered participation directly (GM) or through the parent DPUK cohort (CSR). Descriptive statistics will be used to summarise the outcomes relevant to the number of participants engaged with the register. Its sample size is not defined but is limited by the size of the DPUK parent cohorts.Ethics and disseminationThe database was approved by the South Central–Oxford C Research Ethics Committee, reference 18/SC/0268 on the 27th of June 2018 and amended on the 1st of November 2019. The availability of the register to researchers will be disseminated through DPUK’s official communication channels as well as national and international scientific meetings.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…Moreover, a possible extension of the proposed method is integrating clinical or genetic data to derive subtypes that show consistency across different modalities. Lastly, external validation of the claimed AD subtypes to an independent dataset, such as the Dementias Platform UK (DPUK) (Bauermeister et al, 2020), is another future direction.…”

Section: Potential and Challengesmentioning

confidence: 99%

Multi-scale semi-supervised clustering of brain images: deriving disease subtypes

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et al. 2021

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Disease heterogeneity is a significant obstacle to understanding pathological processes and delivering precision diagnostics and treatment. Clustering methods have gained popularity in stratifying patients into subpopulations (i.e., subtypes) of brain diseases using imaging data. However, unsupervised clustering approaches are often confounded by anatomical and functional variations not related to a disease or pathology of interest. Semi-supervised clustering techniques have been proposed to overcome this and, therefore, capture disease-specific patterns more effectively. An additional limitation of both unsupervised and semi-supervised conventional machine learning methods is that they typically model, learn and infer from data at a basis of feature sets pre- defined at a fixed scale or scales (e.g, an atlas-based regions of interest). Herein we propose a novel method, Multi-scAle heteroGeneity analysIs and Clustering (MAGIC), to depict the multi-scale presentation of disease heterogeneity, which builds on a previously proposed semi-supervised clustering method, HYDRA. It derives multi-scale and clinically interpretable feature representations and exploits a double-cyclic optimization procedure to drive inter-scale-consistent disease subtypes or neuroanatomical dimensions effectively. More importantly, to fill in the gap of understanding under what conditions the clustering model can estimate true heterogeneity related to diseases, we conducted extensive and systematic semi-simulated experiments to evaluate the proposed method on a sizeable healthy control sample from the UK Biobank (N=4403). We then applied MAGIC to real imaging data of Alzheimers disease (ADNI, N=1728) to demonstrate its potential and challenges in dissecting the neuroanatomical heterogeneity of brain diseases. Taken together, we aim to provide guidelines on when such analyses can succeed or should be taken with caution. The code of the proposed method is publicly available at https://github.com/anbai106/MAGIC.

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“…The WMC will also contribute data on incidence, prevalence and burden to the Global Burden of Diseases (GBD) Study, 10 11 and provide new multimorbidity phenotypes to e-cohorts with local participants, and phenotyping algorithms to many e-cohorts that use routine data. 12 We expect that findings from these analyses will provide evidence to health policy leads in order to support prevention and the complex healthcare planning and management of multimorbid individuals. Members of the public are embedded in the research team to ensure the resource focuses on issues of concern to the public.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Protocol for the development of the Wales Multimorbidity e-Cohort (WMC): data sources and methods to construct a population-based research platform to investigate multimorbidity

et al. 2021

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IntroductionMultimorbidity is widely recognised as the presence of two or more concurrent long-term conditions, yet remains a poorly understood global issue despite increasing in prevalence.We have created the Wales Multimorbidity e-Cohort (WMC) to provide an accessible research ready data asset to further the understanding of multimorbidity. Our objectives are to create a platform to support research which would help to understand prevalence, trajectories and determinants in multimorbidity, characterise clusters that lead to highest burden on individuals and healthcare services, and evaluate and provide new multimorbidity phenotypes and algorithms to the National Health Service and research communities to support prevention, healthcare planning and the management of individuals with multimorbidity.Methods and analysisThe WMC has been created and derived from multisourced demographic, administrative and electronic health record data relating to the Welsh population in the Secure Anonymised Information Linkage (SAIL) Databank. The WMC consists of 2.9 million people alive and living in Wales on the 1 January 2000 with follow-up until 31 December 2019, Welsh residency break or death. Published comorbidity indices and phenotype code lists will be used to measure and conceptualise multimorbidity.Study outcomes will include: (1) a description of multimorbidity using published data phenotype algorithms/ontologies, (2) investigation of the associations between baseline demographic factors and multimorbidity, (3) identification of temporal trajectories of clusters of conditions and multimorbidity and (4) investigation of multimorbidity clusters with poor outcomes such as mortality and high healthcare service utilisation.Ethics and disseminationThe SAIL Databank independent Information Governance Review Panel has approved this study (SAIL Project: 0911). Study findings will be presented to policy groups, public meetings, national and international conferences, and published in peer-reviewed journals.

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The Dementias Platform UK (DPUK) Data Portal

Cited by 53 publications

References 31 publications

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

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

Multi-scale semi-supervised clustering of brain images: deriving disease subtypes

Protocol for the development of the Wales Multimorbidity e-Cohort (WMC): data sources and methods to construct a population-based research platform to investigate multimorbidity

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