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Xiao C, Pappas I, Aksman LM, O'Bryant SE, Toga AW. Comparison of genetic and health risk factors for mild cognitive impairment and Alzheimer's disease between Hispanic and non-Hispanic white participants. Alzheimers Dement 2023; 19:5086-5094. [PMID: 37104247 DOI: 10.1002/alz.13110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 03/28/2023] [Accepted: 04/03/2023] [Indexed: 04/28/2023]
Abstract
INTRODUCTION The influence of apolipoprotein E (APOE) genotype on mild cognitive impairment (MCI) and Alzheimer's disease (AD) is well studied in the non-Hispanic white (NHW) population but not in the Hispanic population. Additionally, health risk factors such as hypertension, stroke, and depression may also differ between the two populations. METHODS We combined three data sets (National Alzheimer's Coordinating Center [NACC], Alzheimer's Disease Neuroimaging Initiative [ADNI], Health and Aging Brain Study: Health Disparities [HABS-HD]) and compared risk factors for MCI and AD between Hispanic and NHW participants, with a total of 24,268 participants (11.1% Hispanic). RESULTS APOEε4 was associated with fewer all-cause MCI cases in Hispanic participants (Hispanic odds ratio [OR]: 1.114; NHW OR: 1.453), and APOEε2 (Hispanic OR: 1.224; NHW OR: 0.592) and depression (Hispanic OR: 2.817; NHW OR: 1.847) were associated with more AD cases in Hispanic participants. DISCUSSION APOEε2 may not be protective for AD in Hispanic participants and Hispanic participants with depression may face a higher risk for AD. HIGHLIGHTS GAAIN allows for discovery of data sets to use in secondary analyses. APOEε2 was not protective for AD in Hispanic participants. APOEε4 was associated with fewer MCI cases in Hispanic participants. Depression was associated with more AD cases in Hispanic participants.
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Affiliation(s)
- Cally Xiao
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Ioannis Pappas
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Leon M Aksman
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Sid E O'Bryant
- Institute for Translational Research, University of North Texas Health Science Center, Fort Worth, Texas, USA
| | - Arthur W Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
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Liu CF, Leigh R, Johnson B, Urrutia V, Hsu J, Xu X, Li X, Mori S, Hillis AE, Faria AV. A large public dataset of annotated clinical MRIs and metadata of patients with acute stroke. Sci Data 2023; 10:548. [PMID: 37607929 PMCID: PMC10444746 DOI: 10.1038/s41597-023-02457-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 08/09/2023] [Indexed: 08/24/2023] Open
Abstract
To extract meaningful and reproducible models of brain function from stroke images, for both clinical and research proposes, is a daunting task severely hindered by the great variability of lesion frequency and patterns. Large datasets are therefore imperative, as well as fully automated image post-processing tools to analyze them. The development of such tools, particularly with artificial intelligence, is highly dependent on the availability of large datasets to model training and testing. We present a public dataset of 2,888 multimodal clinical MRIs of patients with acute and early subacute stroke, with manual lesion segmentation, and metadata. The dataset provides high quality, large scale, human-supervised knowledge to feed artificial intelligence models and enable further development of tools to automate several tasks that currently rely on human labor, such as lesion segmentation, labeling, calculation of disease-relevant scores, and lesion-based studies relating function to frequency lesion maps.
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Affiliation(s)
- Chin-Fu Liu
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Richard Leigh
- Department of Neurology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Brenda Johnson
- Department of Neurology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Victor Urrutia
- Department of Neurology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Johnny Hsu
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Xin Xu
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Xin Li
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Susumu Mori
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Argye E Hillis
- Department of Neurology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
- Department of Physical Medicine & Rehabilitation, and Department of Cognitive Science, Johns Hopkins University, Baltimore, MD, USA
| | - Andreia V Faria
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
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Toga AW, Phatak M, Pappas I, Thompson S, McHugh CP, Clement MHS, Bauermeister S, Maruyama T, Gallacher J. The pursuit of approaches to federate data to accelerate Alzheimer's disease and related dementia research: GAAIN, DPUK, and ADDI. Front Neuroinform 2023; 17:1175689. [PMID: 37304174 PMCID: PMC10248126 DOI: 10.3389/fninf.2023.1175689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 05/02/2023] [Indexed: 06/13/2023] Open
Abstract
There is common consensus that data sharing accelerates science. Data sharing enhances the utility of data and promotes the creation and competition of scientific ideas. Within the Alzheimer's disease and related dementias (ADRD) community, data types and modalities are spread across many organizations, geographies, and governance structures. The ADRD community is not alone in facing these challenges, however, the problem is even more difficult because of the need to share complex biomarker data from centers around the world. Heavy-handed data sharing mandates have, to date, been met with limited success and often outright resistance. Interest in making data Findable, Accessible, Interoperable, and Reusable (FAIR) has often resulted in centralized platforms. However, when data governance and sovereignty structures do not allow the movement of data, other methods, such as federation, must be pursued. Implementation of fully federated data approaches are not without their challenges. The user experience may become more complicated, and federated analysis of unstructured data types remains challenging. Advancement in federated data sharing should be accompanied by improvement in federated learning methodologies so that federated data sharing becomes functionally equivalent to direct access to record level data. In this article, we discuss federated data sharing approaches implemented by three data platforms in the ADRD field: Dementia's Platform UK (DPUK) in 2014, the Global Alzheimer's Association Interactive Network (GAAIN) in 2012, and the Alzheimer's Disease Data Initiative (ADDI) in 2020. We conclude by addressing open questions that the research community needs to solve together.
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Affiliation(s)
- Arthur W. Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
| | - Mukta Phatak
- Alzheimer’s Disease Data Initiative, Kirkland, WA, United States
| | - Ioannis Pappas
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
| | - Simon Thompson
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, United Kingdom
| | | | | | - Sarah Bauermeister
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, United Kingdom
| | | | - John Gallacher
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, United Kingdom
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Redolfi A, Archetti D, De Francesco S, Crema C, Tagliavini F, Lodi R, Ghidoni R, Gandini Wheeler-Kingshott CAM, Alexander DC, D'Angelo E. Italian, European, and international neuroinformatics efforts: An overview. Eur J Neurosci 2022. [PMID: 36310103 DOI: 10.1111/ejn.15854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 10/18/2022] [Accepted: 10/18/2022] [Indexed: 12/15/2022]
Abstract
Neuroinformatics is a research field that focusses on software tools capable of identifying, analysing, modelling, organising and sharing multiscale neuroscience data. Neuroinformatics has exploded in the last two decades with the emergence of the Big Data phenomenon, characterised by the so-called 3Vs (volume, velocity and variety), which provided neuroscientists with an improved ability to acquire and process data faster and more cheaply thanks to technical improvements in clinical, genomic and radiological technologies. This situation has led to a 'data deluge', as neuroscientists can routinely collect more study data in a few days than they could in a year just a decade ago. To address this phenomenon, several neuroimaging-focussed neuroinformatics platforms have emerged, funded by national or transnational agencies, with the following goals: (i) development of tools for archiving and organising analytical data (XNAT, REDCap and LabKey); (ii) development of data-driven models evolving from reductionist approaches to multidimensional models (RIN, IVN, HBD, EuroPOND, E-DADS and GAAIN BRAIN); and (iii) development of e-infrastructures to provide sufficient computational power and storage resources (neuGRID, HBP-EBRAINS, LONI and CONP). Although the scenario is still fragmented, there are technological and economical attempts at both national and international levels to introduce high standards for open and Findable, Accessible, Interoperable and Reusable (FAIR) neuroscience worldwide.
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Affiliation(s)
- Alberto Redolfi
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Damiano Archetti
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Silvia De Francesco
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Claudio Crema
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Fabrizio Tagliavini
- Scientific Directorate, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Raffaele Lodi
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy.,Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Roberta Ghidoni
- Molecular Markers Laboratory, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Claudia A M Gandini Wheeler-Kingshott
- NMR Research Unit, Queen Square MS Center, Department of Neuroinflammation, UCL Institute of Neurology, London, UK.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Daniel C Alexander
- Centre for Medical Image Computing, University College London, London, UK.,Department of Computer Science, University College London, London, UK
| | - Egidio D'Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
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Kang M, Cheon BK, Hahn MJ, Seo SW, Cho J, Shin SY, Na DL, Cho J, Choi SH, Kang D. Developing a Dementia Platform Databank Using Multiple Existing Cohorts. Yonsei Med J 2021; 62:1062-1068. [PMID: 34672140 PMCID: PMC8542465 DOI: 10.3349/ymj.2021.62.11.1062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 08/11/2021] [Accepted: 08/17/2021] [Indexed: 11/27/2022] Open
Abstract
This study was conducted as a pilot project to evaluate the feasibility of building an integrate dementia platform converging preexisting dementia cohorts from several variable levels. The following four cohorts were used to develop this pilot platform: 1) Clinical Research Center for Dementia of South Korea (CREDOS), 2) Korean Brain Aging Study for Early Diagnosis and Prediction of Alzheimer's disease (K-BASE), 3) Environmental Pollution-induced Neurological Effects (EPINEF) study, and 4) a prospective registry in Dementia Platform Korea project (DPKR). A total of 29916 patients were included in the platform with 348 integrated variables. Among participants, 13.9%, 31.5%, and 44.2% of patients had normal cognition, mild cognitive impairment, and dementia, respectively. The mean age was 72.4 years. Females accounted for 65.7% of all patients. Those with college or higher education and those without problems in reading or writing accounted for 12.3% and 46.8%, respectively. Marital status, cohabitation, family history of Parkinson's disease, smoking and drinking status, physical activity, sleep status, and nutrition status had rates of missing information of 50% or more. Although individual cohorts were of the same domain and of high quality, we found there were several barriers to integrating individual cohorts, including variability in study variables and measurements. Although many researchers are trying to combine pre-existing cohorts, the process of integrating past data has not been easy. Therefore, it is necessary to establish a protocol with considerations for data integration at the cohort establishment stage.
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Affiliation(s)
- Minwoong Kang
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Korea
- Center for Clinical Epidemiology, Samsung Medical Center, Seoul, Korea
| | - Bo Kyoung Cheon
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Min Jung Hahn
- Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Sang Won Seo
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Korea
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Juhee Cho
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Korea
- Center for Clinical Epidemiology, Samsung Medical Center, Seoul, Korea
- Department of Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, Korea
| | - Soo-Yong Shin
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Korea
- Center for Research Resource Standardization, Samsung Medical Center, Seoul, Korea
| | - Duk L Na
- Neuroscience Center, Samsung Medical Center, Seoul, Korea
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Jaelim Cho
- Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, Korea
| | - Seong Hye Choi
- Department of Neurology, Inha University School of Medicine, Incheon, Korea
| | - Danbee Kang
- Center for Clinical Epidemiology, Samsung Medical Center, Seoul, Korea
- Department of Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, Korea.
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Morris R, Armbruster K, Silva J, Widell DJ, Cheng F. The Association between the Usage of Non-Steroidal Anti-Inflammatory Drugs and Cognitive Status: Analysis of Longitudinal and Cross-Sectional Studies from the Global Alzheimer's Association Interactive Network and Transcriptomic Data. Brain Sci 2020; 10:brainsci10120961. [PMID: 33321871 PMCID: PMC7763310 DOI: 10.3390/brainsci10120961] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 12/05/2020] [Accepted: 12/07/2020] [Indexed: 01/09/2023] Open
Abstract
The degenerative cognitive and physical decline of Alzheimer patients, coupled with the extensive psychological and economic tolls imposed on family members that serve as caretakers, necessitate the discovery of effective cures and preventative measures for age-related cognitive depreciation. In the journey of Alzheimer’s disease treatment discovery, several cross-sectional and longitudinal studies have delineated a noticeable association between the use of nonsteroidal anti-inflammatory drugs (NSAIDs), a class of low-cost drugs with minimal side effects, and the alleviation of age-related memory impairment. In this study, four datasets (two cross-sectional and two longitudinal studies) derived from the Global Alzheimer’s Association Interactive Network (GAAIN) were analyzed. The significant association between the usage of NSAIDs and better cognitive status was observed. The results agree with the findings of previous studies that the use of NSAIDs may be beneficial in the early stages of Alzheimer’s disease. Transcriptomic data show that ibuprofen treatment results in upregulation of several genes involved in arachidonic acid metabolism including PPARγ, Cyp4a12b, Cyp2c66, and Cyp2c37 in the hippocampus. The increase in conversion of arachidonic acid into anti-inflammatory 16C and 18C dicarboxylic acids as well as epoxyeicosatrienoic acids may play a role in reducing the risk of Alzheimer’s disease development.
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Affiliation(s)
- Robert Morris
- Department of Pharmaceutical Science, College of Pharmacy, University of South Florida, Tampa, FL 33613, USA;
| | - Kyle Armbruster
- Biomedical Science Program, College of Art and Science, University of South Florida, Tampa, FL 33620, USA; (K.A.); (J.S.); (D.J.W.)
| | - Julianna Silva
- Biomedical Science Program, College of Art and Science, University of South Florida, Tampa, FL 33620, USA; (K.A.); (J.S.); (D.J.W.)
| | - Daniel James Widell
- Biomedical Science Program, College of Art and Science, University of South Florida, Tampa, FL 33620, USA; (K.A.); (J.S.); (D.J.W.)
| | - Feng Cheng
- Department of Pharmaceutical Science, College of Pharmacy, University of South Florida, Tampa, FL 33613, USA;
- Correspondence: ; Tel.: +1-813-974-4288
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Ibanez A, Flichtentrei D, Hesse E, Dottori M, Tomio A, Slachevsky A, Serrano CM, Gonzalez‐Billaut C, Custodio N, Miranda C, Bustin J, Cetckovitch M, Torrente F, Olavarria L, Leon T, Beber BC, Bruki S, Suemoto CK, Nitrini R, Miller BL, Yokoyama JS. The power of knowledge about dementia in Latin America across health professionals working on aging. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2020; 12:e12117. [PMID: 33088898 PMCID: PMC7560513 DOI: 10.1002/dad2.12117] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 09/01/2020] [Accepted: 09/16/2020] [Indexed: 12/12/2022]
Abstract
INTRODUCTION Expert knowledge is critical to fight dementia in inequitable regions like Latin American and Caribbean countries (LACs). However, the opinions of aging experts on public policies' accessibility and transmission, stigma, diagnostic manuals, data-sharing platforms, and use of behavioral insights (BIs) are not well known. METHODS We investigated opinions among health professionals working on aging in LACs (N = 3365) with regression models including expertise-related information (public policies, BI), individual differences (work, age, academic degree), and location. RESULTS Experts specified low public policy knowledge (X2 = 41.27, P < .001), high levels of stigma (X2 = 2636.37, P < .001), almost absent BI knowledge (X2 = 56.58, P < .001), and needs for regional diagnostic manuals (X2 = 2893.63, df = 3, P < .001) and data-sharing platforms (X2 = 1267.5, df = 3, P < .001). Lack of dementia knowledge was modulated by different factors. An implemented BI-based treatment for a proposed prevention program improved perception across experts. DISCUSSION Our findings help to prioritize future potential actions of governmental agencies and non-governmental organizations (NGOs) to improve LACs' dementia knowledge.
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Affiliation(s)
- Agustin Ibanez
- Global Brain Health Institute and the Memory and Aging Center, Weill Institute for Neurosciences, Department of NeurologyUniversity of California, San Francisco (UCSF)San FranciscoCaliforniaUSA
- Universidad de San AndrésBuenos AiresArgentina
- National Scientific and Technical Research Council (CONICET)Buenos AiresArgentina
- Center for Social and Cognitive Neuroscience (CSCN), School of PsychologyUniversidad Adolfo IbáñezSantiago de ChileChile
- Universidad Autónoma del CaribeBarranquillaColombia
| | | | - Eugenia Hesse
- Universidad de San AndrésBuenos AiresArgentina
- National Scientific and Technical Research Council (CONICET)Buenos AiresArgentina
| | - Martin Dottori
- Universidad de San AndrésBuenos AiresArgentina
- National Scientific and Technical Research Council (CONICET)Buenos AiresArgentina
| | - Ailin Tomio
- Universidad de San AndrésBuenos AiresArgentina
| | - Andrea Slachevsky
- Memory and Neuropsychiatric Clinic (CMYN), Neurology DepartmentDel Salvador Hospital and University of Chile Faculty of MedicineSantiagoChile
- Geroscience Center for Brain Health and Metabolism (GERO), Faculty of MedicineUniversity of ChileSantiagoChile
- Neuropsychology and Clinical Neuroscience Laboratory (LANNEC), Physiopathology Department ‐ ICBM, Neuroscience and East Neuroscience Departments, Faculty of MedicineUniversity of ChileSantiagoChile
- Department of Neurology and PsychiatryClínica Alemana‐Universidad del DesarrolloSantiagoChile
| | - Cecilia M Serrano
- Cognitive Neurology, Neurology DepartmentDr César Milstein HospitalBuenos AiresArgentina
| | - Christian Gonzalez‐Billaut
- Geroscience Center for Brain Health and Metabolism (GERO), Faculty of MedicineUniversity of ChileSantiagoChile
| | - Nilton Custodio
- Unit Cognitive Impairment and Dementia Prevention, Cognitive Neurology CenterPeruvian Institute of NeurosciencesLimaPerú
| | - Claudia Miranda
- Faculty of NursingUniversidad Andres BelloSantiagoChile
- Millennium Institute for Research in Depression and PersonalitySantiagoChile
| | - Julian Bustin
- Institute of Translational and Cognitive Neuroscience (INCYT), INECO Foundation, Favaloro UniversityNational Scientific and Technical Research Council (CONICET)Buenos AiresArgentina
| | - Marcelo Cetckovitch
- Institute of Translational and Cognitive Neuroscience (INCYT), INECO Foundation, Favaloro UniversityNational Scientific and Technical Research Council (CONICET)Buenos AiresArgentina
| | - Fernando Torrente
- Institute of Translational and Cognitive Neuroscience (INCYT), INECO Foundation, Favaloro UniversityNational Scientific and Technical Research Council (CONICET)Buenos AiresArgentina
| | - Loreto Olavarria
- Memory and Neuropsychiatric Clinic (CMYN), Neurology DepartmentDel Salvador Hospital and University of Chile Faculty of MedicineSantiagoChile
| | - Tomas Leon
- Memory and Neuropsychiatric Clinic (CMYN), Neurology DepartmentDel Salvador Hospital and University of Chile Faculty of MedicineSantiagoChile
| | - Barbara Costa Beber
- Department of Speech and Language Pathology, Federal University of Health Sciences of Porto Alegre (UFCSPA)Atlantic Fellow for Equity in Brain HealthPorto AlegreBrazil
| | - Sonia Bruki
- Faculdade de MedicinaUniversidade de São PauloSão PauloBrazil
| | | | - Ricardo Nitrini
- Faculdade de MedicinaUniversidade de São PauloSão PauloBrazil
| | - Bruce L. Miller
- Global Brain Health Institute and the Memory and Aging Center, Weill Institute for Neurosciences, Department of NeurologyUniversity of California, San Francisco (UCSF)San FranciscoCaliforniaUSA
| | - Jennifer S. Yokoyama
- Global Brain Health Institute and the Memory and Aging Center, Weill Institute for Neurosciences, Department of NeurologyUniversity of California, San Francisco (UCSF)San FranciscoCaliforniaUSA
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Samra H, Li A, Soh B, Zain MA. Utilisation of hospital information systems for medical research in Saudi Arabia: A mixed-method exploration of the views of healthcare and IT professionals involved in hospital database management systems. Health Inf Manag 2019; 49:117-126. [PMID: 31046465 DOI: 10.1177/1833358319847120] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Although in recent times the Saudi government has paid much attention to the adaptation of hospital information systems (HIS) and electronic medical records (EMR), the importance of utilising HIS to enhance medical research has been neglected. OBJECTIVE We aimed to (i) investigate the current state of medical research in Saudi Arabia, (ii) identify possible issues that hinder improvement of medical research and (iii) identify possible solutions to enhance the role of HIS in medical research in Saudi Arabia. METHOD We used a questionnaire and structured interview approach. Questionnaires were distributed to Saudi healthcare professionals. One hundred responses to our questionnaire were captured by the online Google Form designed specifically for our survey. Structured interviews with two IT professionals were conducted regarding technical aspects of their hospital data management systems. RESULTS Six themes contributing to the inefficacy of HIS in medical research in Saudi Arabia emerged from the data: incorrect datasets, difficult data collection and storage, poor data analytics, a lack of system interoperability across different HIS for universal access and negative perception of the usefulness of HIS for medical research. CONCLUSION AND IMPLICATIONS Our findings suggest (i) cloud-based HIS would support efficient, reliable and integrated data collection and storage across all hospitals in Saudi Arabia; (ii) EMR data sources should be seamlessly linked to avoid incomplete, fragmented or erroneous EMR in Saudi Arabia; and (iii) collaboration between all hospitals in Saudi Arabia to adopt a uniform standard to support interoperability and improve data exchange and integration is necessary.
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Affiliation(s)
- Halima Samra
- La Trobe University, Australia.,King Abdulaziz University, Kingdom of Saudi Arabia
| | | | - Ben Soh
- La Trobe University, Australia
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Trifan A, Oliveira JL. Patient data discovery platforms as enablers of biomedical and translational research: A systematic review. J Biomed Inform 2019; 93:103154. [PMID: 30922867 DOI: 10.1016/j.jbi.2019.103154] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Revised: 03/15/2019] [Accepted: 03/18/2019] [Indexed: 11/28/2022]
Abstract
BACKGROUND The global shift from paper health records to electronic ones has led to an impressive growth of biomedical digital data along the past two decades. Exploring and extracting knowledge from these data has the potential to enhance translational research and lead to positive outcomes for the population's health and healthcare. OBECTIVE The aim of this study was to conduct a systematic review to identify software platforms that enable discovery, secondary use and interoperability of biomedical data. Additionally, we aim evaluating the identified solutions in terms of clinical interest and main healthcare-related outcomes. METHODS A systematic search of the scientific literature published and indexed in Pubmed between January 2014 and September 2018 was performed. Inclusion criteria were as follows: relevance for the topic of biomedical data discovery, English language, and free full text. To increase the recall, we developed a semi-automatic and incremental methodology to retrieve articles that cite one or more of the previous set. RESULTS A total number of 500 candidate papers were retrieved through this methodology. Of these, 85 were eligible for abstract assessment. Finally, 37 studies qualified for a full-text review, and 20 provided enough information for the study objectives. CONCLUSIONS This study revealed that biomedical discovery platforms are both a current necessity and a significantly innovative agent in the area of healthcare. The outcomes that were identified, in terms of scientific publications, clinical studies and research collaborations stand as evidence.
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Predication of different stages of Alzheimer’s disease using neighborhood component analysis and ensemble decision tree. J Neurosci Methods 2018; 302:35-41. [DOI: 10.1016/j.jneumeth.2018.02.014] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2017] [Revised: 02/18/2018] [Accepted: 02/20/2018] [Indexed: 11/18/2022]
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11
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Zhang Y, Wang P. Age-Related Increase of Insulin-Degrading Enzyme Is Inversely Correlated with Cognitive Function in APPswe/PS1dE9 Mice. Med Sci Monit 2018; 24:2446-2455. [PMID: 29680859 PMCID: PMC5935016 DOI: 10.12659/msm.909596] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Background Insulin-degrading enzyme (IDE) is an important regulator for Aβ clearance and diabetes. Although it is indispensable in removing plaques related to onset Alzheimer’s disease (AD) and in degrading insulin related to diabetes, there have been few studies on the dynamic level of IDE in different stages of AD. Material/Methods The present study explored the level IDE protein in different stages of APPswe/PS1dE9 mice and their correlations with cognitive decline. The 4-month-old, 10-month-old, and 18-month-old mice were used as the different age stages of mice. Cognitive function was evaluated using the Morris water maze test. We also observed the level of Aβ plaques in brain regions of different stages. Results The data revealed that the expression of IDE was dramatically higher than in age-matched wild mice at the age of 10 months and 18 months. In terms of distribution, Aβ plaques were deposited mostly in the cortex and hippocampus, especially in 10-month-old and 18-month-old APPswe/PS1dE9 mice. The cognitive function of 4-month-old APPswe/PS1dE9 mice was not significantly differ in spatial learning. However, the cognitive function, both spatial learning and spatial memory, was dramatically lower in 10-month-old and 18-month-old groups. Conclusions There was a positive correlation between the expression of IDE and spatial memory in 10-month-old and 18-month-old APPswe/PS1dE9 mice. The study of this protein may provide reference values for the further study of IDE in Alzheimer’s disease.
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Affiliation(s)
- Yi Zhang
- Department of Clinical Laboratory, Xuanwu Hospital, Capital Medical University, Beijing, China (mainland)
| | - Peichang Wang
- Department of Clinical Laboratory, Xuanwu Hospital, Capital Medical University, Beijing, China (mainland)
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Sarica A, Cerasa A, Quattrone A, Calhoun V. Editorial on special issue: Machine learning on MCI. J Neurosci Methods 2018; 302:1-2. [PMID: 29581008 DOI: 10.1016/j.jneumeth.2018.03.011] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Affiliation(s)
- Alessia Sarica
- Neuroscience Research Center, Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Italy.
| | - Antonio Cerasa
- IBFM, National Research Council, Catanzaro, Italy; S. Anna Institute and Research in Advanced Neurorehabilitation (RAN), Crotone, Italy
| | - Aldo Quattrone
- Neuroscience Research Center, Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Italy; IBFM, National Research Council, Catanzaro, Italy; Institute of Neurology, University Magna Graecia, Catanzaro, Italy
| | - Vince Calhoun
- The Mind Research Network, Albuquerque, NM, USA; The Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA
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Neu SC, Pa J, Kukull W, Beekly D, Kuzma A, Gangadharan P, Wang LS, Romero K, Arneric SP, Redolfi A, Orlandi D, Frisoni GB, Au R, Devine S, Auerbach S, Espinosa A, Boada M, Ruiz A, Johnson SC, Koscik R, Wang JJ, Hsu WC, Chen YL, Toga AW. Apolipoprotein E Genotype and Sex Risk Factors for Alzheimer Disease: A Meta-analysis. JAMA Neurol 2017; 74:1178-1189. [PMID: 28846757 PMCID: PMC5759346 DOI: 10.1001/jamaneurol.2017.2188] [Citation(s) in RCA: 419] [Impact Index Per Article: 59.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Importance It is unclear whether female carriers of the apolipoprotein E (APOE) ε4 allele are at greater risk of developing Alzheimer disease (AD) than men, and the sex-dependent association of mild cognitive impairment (MCI) and APOE has not been established. Objective To determine how sex and APOE genotype affect the risks for developing MCI and AD. Data Sources Twenty-seven independent research studies in the Global Alzheimer's Association Interactive Network with data on nearly 58 000 participants. Study Selection Non-Hispanic white individuals with clinical diagnostic and APOE genotype data. Data Extraction and Synthesis Homogeneous data sets were pooled in case-control analyses, and logistic regression models were used to compute risks. Main Outcomes and Measures Age-adjusted odds ratios (ORs) and 95% confidence intervals for developing MCI and AD were calculated for men and women across APOE genotypes. Results Participants were men and women between ages 55 and 85 years. Across data sets most participants were white, and for many participants, racial/ethnic information was either not collected or not known. Men (OR, 3.09; 95% CI, 2.79-3.42) and women (OR, 3.31; CI, 3.03-3.61) with the APOE ε3/ε4 genotype from ages 55 to 85 years did not show a difference in AD risk; however, women had an increased risk compared with men between the ages of 65 and 75 years (women, OR, 4.37; 95% CI, 3.82-5.00; men, OR, 3.14; 95% CI, 2.68-3.67; P = .002). Men with APOE ε3/ε4 had an increased risk of AD compared with men with APOE ε3/ε3. The APOE ε2/ε3 genotype conferred a protective effect on women (OR, 0.51; 95% CI, 0.43-0.61) decreasing their risk of AD more (P value = .01) than men (OR, 0.71; 95% CI, 0.60-0.85). There was no difference between men with APOE ε3/ε4 (OR, 1.55; 95% CI, 1.36-1.76) and women (OR, 1.60; 95% CI, 1.43-1.81) in their risk of developing MCI between the ages of 55 and 85 years, but women had an increased risk between 55 and 70 years (women, OR, 1.43; 95% CI, 1.19-1.73; men, OR, 1.07; 95% CI, 0.87-1.30; P = .05). There were no significant differences between men and women in their risks for converting from MCI to AD between the ages of 55 and 85 years. Individuals with APOE ε4/ε4 showed increased risks vs individuals with ε3/ε4, but no significant differences between men and women with ε4/ε4 were seen. Conclusions and Relevance Contrary to long-standing views, men and women with the APOE ε3/ε4 genotype have nearly the same odds of developing AD from age 55 to 85 years, but women have an increased risk at younger ages.
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Affiliation(s)
- Scott C Neu
- Laboratory of Neuro Imaging, Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles
| | - Judy Pa
- Laboratory of Neuro Imaging, Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles
| | - Walter Kukull
- National Alzheimer's Coordinating Center, University of Washington, Seattle
| | - Duane Beekly
- National Alzheimer's Coordinating Center, University of Washington, Seattle
| | - Amanda Kuzma
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia
| | | | - Li-San Wang
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia
| | - Klaus Romero
- Coalition Against Major Disease, Critical Path Institute, Tucson, Arizona
| | - Stephen P Arneric
- Coalition Against Major Disease, Critical Path Institute, Tucson, Arizona
| | - Alberto Redolfi
- IRCCS Fatebenefratelli, The National Centre for Alzheimer's Disease, Brescia, Italy
| | - Daniele Orlandi
- IRCCS Fatebenefratelli, The National Centre for Alzheimer's Disease, Brescia, Italy
| | - Giovanni B Frisoni
- IRCCS Fatebenefratelli, The National Centre for Alzheimer's Disease, Brescia, Italy
- University Hospitals and University of Geneva, Geneva, Switzerland
| | - Rhoda Au
- Department of Anatomy and Neurobiology, Boston University Schools of Medicine and Public Health, Boston, Massachusetts
- Department Neurology and Epidemiology, Boston University Schools of Medicine and Public Health, Boston, Massachusetts
- Framingham Heart Study, Boston University Schools of Medicine and Public Health, Boston, Massachusetts
| | - Sherral Devine
- Department of Neurology, Framingham Heart Study, Boston University School of Medicine, Boston, Massachusetts
| | - Sanford Auerbach
- Department of Neurology, Framingham Heart Study, Boston University School of Medicine, Boston, Massachusetts
| | - Ana Espinosa
- Research Center and Memory Clinic, Fundació ACE, Institut Català de Neurociències Aplicades, Barcelona, Spain
| | - Mercè Boada
- Research Center and Memory Clinic, Fundació ACE, Institut Català de Neurociències Aplicades, Barcelona, Spain
| | - Agustín Ruiz
- Research Center and Memory Clinic, Fundació ACE, Institut Català de Neurociències Aplicades, Barcelona, Spain
| | | | - Rebecca Koscik
- University of Wisconsin School of Medicine and Public Health, Madison
| | - Jiun-Jie Wang
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan City, Taiwan
- Neuroscience Research Center, Chang Gung Memorial Hospital at Linkou, Taoyuan City, Taiwan
| | - Wen-Chuin Hsu
- Department of Neurology, Chang Gung Memorial Hospital at Linkou, Taoyuan City, Taiwan
- Dementia Center, Chang Gung Memorial Hospital at Linkou, Taoyuan City, Taiwan
| | - Yao-Liang Chen
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan City, Taiwan
- Department of Medical Imaging and Intervention, Keelung Branch, Chang Gung Memorial Hospital, Keelung City, Taiwan
| | - Arthur W Toga
- Laboratory of Neuro Imaging, Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles
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Abstract
Cloud computing, at its most fundamental level, is a technology that allows computing to occur as an on-demand utility. Personalized medicine is revolutionizing the field of medicine, and aims to provide individualized treatment for patients, based on their genetic material and medical history, through comparisons to genetic material from thousands of other patients. Better patient outcomes are expected in a personalized medicine domain as compared to more generic generalized options. In this paper the authors explore the use of cloud computing in the area of personalized medicine examining unique benefits and opportunities. They also recognize and discuss the presence of some inherent risks. Using the cloud for personalized medicine can expedite collaborations and data collection across different clinicians, researchers and other stakeholders involved in the fight against diseases. This paper examines key elements associated with using cloud computing in the specialized area of personalized medicine.
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Affiliation(s)
| | - Patrick Brown
- Thermo Scientific NanoDrop Products, Wilmington, DE, USA
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15
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Toga AW, Neu SC, Bhatt P, Crawford KL, Ashish N. The Global Alzheimer's Association Interactive Network. Alzheimers Dement 2016; 12:49-54. [PMID: 26318022 PMCID: PMC4817494 DOI: 10.1016/j.jalz.2015.06.1896] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2015] [Revised: 06/22/2015] [Accepted: 06/30/2015] [Indexed: 01/22/2023]
Abstract
INTRODUCTION The Global Alzheimer's Association Interactive Network (GAAIN) is consolidating the efforts of independent Alzheimer's disease data repositories around the world with the goals of revealing more insights into the causes of Alzheimer's disease, improving treatments, and designing preventative measures that delay the onset of physical symptoms. METHODS We developed a system for federating these repositories that is reliant on the tenets that (1) its participants require incentives to join, (2) joining the network is not disruptive to existing repository systems, and (3) the data ownership rights of its members are protected. RESULTS We are currently in various phases of recruitment with over 55 data repositories in North America, Europe, Asia, and Australia and can presently query >250,000 subjects using GAAIN's search interfaces. DISCUSSION GAAIN's data sharing philosophy, which guided our architectural choices, is conducive to motivating membership in a voluntary data sharing network.
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Affiliation(s)
- Arthur W Toga
- Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
| | - Scott C Neu
- Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Priya Bhatt
- Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Karen L Crawford
- Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Naveen Ashish
- Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
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16
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Toga AW, Crawford KL. The Alzheimer's Disease Neuroimaging Initiative informatics core: A decade in review. Alzheimers Dement 2015; 11:832-9. [PMID: 26194316 PMCID: PMC4510464 DOI: 10.1016/j.jalz.2015.04.004] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2015] [Accepted: 04/22/2015] [Indexed: 10/23/2022]
Abstract
The Informatics Core of the Alzheimer's Disease Neuroimaging Initiative has coordinated data integration and dissemination for a continually growing and complex data set in which both data contributors and recipients span institutions, scientific disciplines, and geographic boundaries. This article provides an update on the accomplishments and future plans.
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Affiliation(s)
- Arthur W Toga
- Laboratory of Neuro Imaging (LONI), USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA.
| | - Karen L Crawford
- Laboratory of Neuro Imaging (LONI), USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
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