Diagnostic gap; Early diagnosis; Population ; Brecha de diagnóstico; Diagnóstico precoz; Población ; Bretxa de diagnòstic; Diagnòstic precoç; Població ; Introduction: The Models of Patient Engagement for Alzheimer's Disease (MOPEAD) project was conceived to explore innovative complementary strategies to uncover hidden prodromal and mild Alzheimer's disease (AD) dementia cases and to raise awareness both in the general public and among health professionals about the importance of early diagnosis. Methods: Four different strategies or RUNs were used: (a) a web-based (WB) prescreening tool, (2) an open house initiative (OHI), (3) a primary care-based protocol for early detection of cognitive decline (PC), and (4) a tertiary care-based pre-screening at diabetologist clinics (DC). Results: A total of 1129 patients at high risk of having prodromal AD or dementia were identified of 2847 pre-screened individuals (39.7%). The corresponding proportion for the different initiatives were 36.8% (WB), 35.6% (OHI), 44.4% (PC), and 58.3% (DC). Conclusion: These four complementary pre-screening strategies were useful for identifying individuals at high risk of having prodromal or mild AD. ; This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under Grant Agreement No 115985. This Joint Undertaking receives support from the European Union's Horizon 2020 Research and Innovation program and the European Federation of Pharmaceutical Industries and Associations. www.imi.europa.eu/. All participants provided informed consent.
Introduction: The value of quantitative longitudinal and regional amyloid beta (Aβ) measurements in predicting cognitive decline in initially cognitively unimpaired (CU) individuals remains to be determined. Methods: We selected 133 CU individuals with two or more [11C]Pittsburgh compound B ([11C]PiB) scans and neuropsychological data from Open Access Series of Imaging Studies (OASIS-3). Baseline and annualized distribution volume ratios were computed for a global composite and four regional clusters. The predictive value of Aβ measurements (baseline, slope, and interaction) on longitudinal cognitive performance was examined. Results: Global performance could only be predicted by Aβ burden in an early cluster (precuneus, lateral orbitofrontal, and insula) and the precuneus region of interest (ROI) by itself significantly improved the model. Precuneal Aβ burden was also predictive of immediate and delayed episodic memory performance. In Aβ subjects at baseline (N = 93), lateral orbitofrontal Aβ burden predicted working and semantic memory performance. Discussion: Quantifying longitudinal and regional changes in Aβ can improve the prediction of cognitive functioning in initially CU individuals. ; This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No. 115952. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation program and EFPIA. This communication reflects the views of the authors and neither IMI nor the European Union and EFPIA are liable for any use that may be made of the information contained herein. FB is supported by the NIHR UCLH biomedical research center. FB and AMW are supported by the European Union's Horizon 2020 research and innovation program under grant agreement No. 666992. Data were provided by OASIS‐3. Principal Investigators: T. Benzinger, D. Marcus, J. Morris; NIH P50AG00561, P30NS09857781, P01AG026276, P01AG003991, R01AG043434, UL1TR000448, and R01EB009352.
Objectives: Social dysfunction is one of the most common signs of major neuropsychiatric disorders. The Default Mode Network (DMN) is crucially implicated in both psychopathology and social dysfunction, although the transdiagnostic properties of social dysfunction remains unknown. As part of the pan-European PRISM (Psychiatric Ratings using Intermediate Stratified Markers) project, we explored cross-disorder impact of social dysfunction on DMN connectivity. Methods: We studied DMN intrinsic functional connectivity in relation to social dysfunction by applying Independent Component Analysis and Dual Regression on resting-state fMRI data, among schizophrenia (SZ; N=48), Alzheimer disease (AD; N=47) patients and healthy controls (HC; N=55). Social dysfunction was operationalised via the Social Functioning Scale (SFS) and De Jong-Gierveld Loneliness Scale (LON). Results: Both SFS and LON were independently associated with diminished DMN connectional integrity within rostromedial prefrontal DMN subterritories (pcorrected range=0.02–0.04). The combined effect of these indicators (Mean.SFS + LON) on diminished DMN connectivity was even more pronounced (both spatially and statistically), independent of diagnostic status, and not confounded by key clinical or sociodemographic effects, comprising large sections of rostromedial and dorsomedial prefrontal cortex (pcorrected =0.01). Conclusions: These findings pinpoint DMN connectional alterations as putative transdiagnostic endophenotypes for social dysfunction and could aid personalised care initiatives grounded in social behaviour ; The project leading to this application has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 115916. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA. This publication reflects only the author's views and neither the IMI 2JU nor EFPIA nor the European Commission are liable for any use that may be made of the ...
In: Lovestone , S , Vannieuwenhuyse , B , Visser , P-J , Bos , I , Vos , S , Streffer , J , Smith , U , Waterworth , D , van der Lei , J , Rijnbeek , P , Oliveira , J L , Van Speybroeck , M , Verbeeck , R , Kalra , D , Lea , N , Molero , E , Lewi , M , Alexander , M , James , G , Sage , C , Praet , J , Egger , P , Singh , G , Hughes , N & EMIF Consortium 2020 , ' The European medical information framework : A novel ecosystem for sharing healthcare data across Europe ' , Learning health systems , vol. 4 , no. 2 , e10214 . https://doi.org/10.1002/lrh2.10214
Introduction The European medical information framework (EMIF) was an Innovative Medicines Initiative project jointly supported by the European Union and the European Federation of Pharmaceutical Industries and Associations, that generated a common technology and governance framework to identify, assess and (re)use healthcare data, to facilitate real-world data research. The objectives of EMIF included providing a unified platform to support a wide range of studies within two verification programmes-Alzheimer's disease (EMIF-AD), and metabolic consequences of obesity (EMIF-MET). Methods The EMIF platform was built around two main data-types: electronic health record data and research cohort data, and the platform architecture composed of a set of tools designed to enable data discovery and characterisation. This included the EMIF catalogue, which allowed users to find relevant data sources, including the data-types collected. Data harmonisation via a common data model were central to the project especially for population data sources. EMIF also developed an ethical code of practice to ensure data protection, patient confidentiality and compliance with the European Data Protection Directive, and GDPR. Results Currently 18 population-based disease agnostic and 60 cohort-based Alzheimer's data partners from across 14 countries are contained within the catalogue, and this will continue to expand. The work conducted in EMIF-AD and EMIF-MET includes standardizing cohorts, summarising baseline characteristics of patients, developing diagnostic algorithms, epidemiological studies, identifying and validating novel biomarkers and selecting potential patient samples for pharmacological intervention. Conclusions EMIF was designed to provide a sustainable model as demonstrated by the sustainability plans for EMIF-AD. Although network-wide studies using EMIF were not conducted during this project to evaluate its sustainability, learning from EMIF will be used in the follow-on IMI-2 project, European Health Data and Evidence ...
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.
In: Bauermeister , S , Orton , C , Thompson , S , Barker , R A , Bauermeister , J R , Ben-Shlomo , Y , Brayne , C , Burn , D , Campbell , A , Calvin , C , Chandran , S , Chaturvedi , N , Chêne , G , Chessell , I P , Corbett , A , Davis , D H J , Denis , M , Dufouil , C , Elliott , P , Fox , N , Hill , D , Hofer , S M , Hu , M T , Jindra , C , Kee , F , Kim , C H , Kim , C , Kivimaki , M , Koychev , I , Lawson , R A , Linden , G J , Lyons , R A , Mackay , C , Matthews , P M , McGuiness , B , Middleton , L , Moody , C , Moore , K , Na , D L , O'Brien , J T , Ourselin , S , Paranjothy , S , Park , K S , Porteous , D J , Richards , M , Ritchie , C W , Rohrer , J D , Rossor , M N , Rowe , J B , Scahill , R , Schnier , C , Schott , J M , Seo , S W , South , M , Steptoe , M , Tabrizi , S J , Tales , A , Tillin , T , Timpson , N J , Toga , A W , Visser , P J , Wade-Martins , R , Wilkinson , T , Williams , J , Wong , A & Gallacher , J E J 2020 , ' The Dementias Platform UK (DPUK) Data Portal ' , European Journal of Epidemiology , vol. 35 , no. 6 , pp. 601-611 . https://doi.org/10.1007/s10654-020-00633-4
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.
In: Bauermeister , S , Orton , C , Thompson , S , Barker , R A , Bauermeister , J R , Ben-Shlomo , Y , Brayne , C , Burn , D , Campbell , A , Calvin , C , Chandran , S , Chaturvedi , N , Chêne , G , Chessell , I P , Corbett , A , Davis , D H J , Denis , M , Dufouil , C , Elliott , P , Fox , N , Hill , D , Hofer , S M , Hu , M T , Jindra , C , Kee , F , Kim , C H , Kim , C , Kivimaki , M , Koychev , I , Lawson , R A , Linden , G J , Lyons , R A , Mackay , C , Matthews , P M , McGuiness , B , Middleton , L , Moody , C , Moore , K , Na , D L , O'Brien , J T , Ourselin , S , Paranjothy , S , Park , K S , Porteous , D J , Richards , M , Ritchie , C W , Rohrer , J D , Rossor , M N , Rowe , J B , Scahill , R , Schnier , C , Schott , J M , Seo , S W , South , M , Steptoe , M , Tabrizi , S J , Tales , A , Tillin , T , Timpson , N J , Toga , A W , Visser , P J , Wade-Martins , R , Wilkinson , T , Williams , J , Wong , A & Gallacher , J E J 2020 , ' The Dementias Platform UK (DPUK) Data Portal ' , European Journal of Epidemiology , vol. 35 , no. 6 , pp. 601-611 . https://doi.org/10.1007/s10654-020-00633-4
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.