The nature of progression in Parkinson's disease: an application of non-linear, multivariate, longitudinal random effects modelling.
Identifieur interne : 000851 ( PubMed/Curation ); précédent : 000850; suivant : 000852The nature of progression in Parkinson's disease: an application of non-linear, multivariate, longitudinal random effects modelling.
Auteurs : Lisa Kuramoto [Canada] ; Jacquelyn Cragg ; Ramachandiran Nandhagopal ; Edwin Mak ; Vesna Sossi ; Raul De La Fuente-Fernández ; A Jon Stoessl ; Michael SchulzerSource :
- PloS one [ 1932-6203 ] ; 2013.
English descriptors
- KwdEn :
- Age Factors, Age of Onset, Algorithms, Asymptomatic Diseases, Cross-Sectional Studies, Disease Progression, Humans, Longitudinal Studies, Models, Statistical, Parkinson Disease (diagnostic imaging), Parkinson Disease (pathology), Parkinson Disease (physiopathology), Putamen (diagnostic imaging), Putamen (pathology), Putamen (physiopathology), Tomography, Emission-Computed.
- MESH :
- diagnostic imaging : Parkinson Disease, Putamen.
- pathology : Parkinson Disease, Putamen.
- physiopathology : Parkinson Disease, Putamen.
- Age Factors, Age of Onset, Algorithms, Asymptomatic Diseases, Cross-Sectional Studies, Disease Progression, Humans, Longitudinal Studies, Models, Statistical, Tomography, Emission-Computed.
Abstract
To date, statistical methods that take into account fully the non-linear, longitudinal and multivariate aspects of clinical data have not been applied to the study of progression in Parkinson's disease (PD). In this paper, we demonstrate the usefulness of such methodology for studying the temporal and spatial aspects of the progression of PD. Extending this methodology further, we also explore the presymptomatic course of this disease.
DOI: 10.1371/journal.pone.0076595
PubMed: 24204641
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first="Ramachandiran" last="Nandhagopal">Ramachandiran Nandhagopal</name></author><author><name sortKey="Mak, Edwin" sort="Mak, Edwin" uniqKey="Mak E" first="Edwin" last="Mak">Edwin Mak</name></author><author><name sortKey="Sossi, Vesna" sort="Sossi, Vesna" uniqKey="Sossi V" first="Vesna" last="Sossi">Vesna Sossi</name></author><author><name sortKey="De La Fuente Fernandez, Raul" sort="De La Fuente Fernandez, Raul" uniqKey="De La Fuente Fernandez R" first="Raul" last="De La Fuente-Fernández">Raul De La Fuente-Fernández</name></author><author><name sortKey="Stoessl, A Jon" sort="Stoessl, A Jon" uniqKey="Stoessl A" first="A Jon" last="Stoessl">A Jon Stoessl</name></author><author><name sortKey="Schulzer, Michael" sort="Schulzer, Michael" uniqKey="Schulzer M" first="Michael" last="Schulzer">Michael Schulzer</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2013">2013</date><idno type="RBID">pubmed:24204641</idno><idno 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& Evaluation, Vancouver Coastal Health Research Institute, Vancouver, British Columbia</wicri:regionArea></affiliation></author><author><name sortKey="Cragg, Jacquelyn" sort="Cragg, Jacquelyn" uniqKey="Cragg J" first="Jacquelyn" last="Cragg">Jacquelyn Cragg</name></author><author><name sortKey="Nandhagopal, Ramachandiran" sort="Nandhagopal, Ramachandiran" uniqKey="Nandhagopal R" first="Ramachandiran" last="Nandhagopal">Ramachandiran Nandhagopal</name></author><author><name sortKey="Mak, Edwin" sort="Mak, Edwin" uniqKey="Mak E" first="Edwin" last="Mak">Edwin Mak</name></author><author><name sortKey="Sossi, Vesna" sort="Sossi, Vesna" uniqKey="Sossi V" first="Vesna" last="Sossi">Vesna Sossi</name></author><author><name sortKey="De La Fuente Fernandez, Raul" sort="De La Fuente Fernandez, Raul" uniqKey="De La Fuente Fernandez R" first="Raul" last="De La Fuente-Fernández">Raul De La Fuente-Fernández</name></author><author><name sortKey="Stoessl, A Jon" sort="Stoessl, A Jon" uniqKey="Stoessl A" first="A Jon" last="Stoessl">A Jon Stoessl</name></author><author><name sortKey="Schulzer, Michael" sort="Schulzer, Michael" uniqKey="Schulzer M" first="Michael" last="Schulzer">Michael Schulzer</name></author></analytic><series><title level="j">PloS one</title><idno type="eISSN">1932-6203</idno><imprint><date when="2013" type="published">2013</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Age Factors</term><term>Age of Onset</term><term>Algorithms</term><term>Asymptomatic Diseases</term><term>Cross-Sectional Studies</term><term>Disease Progression</term><term>Humans</term><term>Longitudinal Studies</term><term>Models, Statistical</term><term>Parkinson Disease (diagnostic imaging)</term><term>Parkinson Disease (pathology)</term><term>Parkinson Disease (physiopathology)</term><term>Putamen (diagnostic imaging)</term><term>Putamen (pathology)</term><term>Putamen (physiopathology)</term><term>Tomography, Emission-Computed</term></keywords><keywords scheme="MESH" qualifier="diagnostic imaging" xml:lang="en"><term>Parkinson Disease</term><term>Putamen</term></keywords><keywords scheme="MESH" qualifier="pathology" xml:lang="en"><term>Parkinson Disease</term><term>Putamen</term></keywords><keywords scheme="MESH" qualifier="physiopathology" xml:lang="en"><term>Parkinson Disease</term><term>Putamen</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Age Factors</term><term>Age of Onset</term><term>Algorithms</term><term>Asymptomatic Diseases</term><term>Cross-Sectional Studies</term><term>Disease Progression</term><term>Humans</term><term>Longitudinal Studies</term><term>Models, Statistical</term><term>Tomography, Emission-Computed</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">To date, statistical methods that take into account fully the non-linear, longitudinal and multivariate aspects of clinical data have not been applied to the study of progression in Parkinson's disease (PD). In this paper, we demonstrate the usefulness of such methodology for studying the temporal and spatial aspects of the progression of PD. Extending this methodology further, we also explore the presymptomatic course of this disease.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">24204641</PMID><DateCreated><Year>2013</Year><Month>11</Month><Day>08</Day></DateCreated><DateCompleted><Year>2014</Year><Month>08</Month><Day>04</Day></DateCompleted><DateRevised><Year>2016</Year><Month>11</Month><Day>25</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Electronic">1932-6203</ISSN><JournalIssue CitedMedium="Internet"><Volume>8</Volume><Issue>10</Issue><PubDate><Year>2013</Year></PubDate></JournalIssue><Title>PloS one</Title><ISOAbbreviation>PLoS ONE</ISOAbbreviation></Journal><ArticleTitle>The nature of progression in Parkinson's disease: an application of non-linear, multivariate, longitudinal random effects modelling.</ArticleTitle><Pagination><MedlinePgn>e76595</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pone.0076595</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">To date, statistical methods that take into account fully the non-linear, longitudinal and multivariate aspects of clinical data have not been applied to the study of progression in Parkinson's disease (PD). In this paper, we demonstrate the usefulness of such methodology for studying the temporal and spatial aspects of the progression of PD. Extending this methodology further, we also explore the presymptomatic course of this disease.</AbstractText><AbstractText Label="METHODS" NlmCategory="METHODS">Longitudinal Positron Emission Tomography (PET) measurements were collected on 78 PD patients, from 4 subregions on each side of the brain, using 3 different radiotracers. Non-linear, multivariate, longitudinal random effects modelling was applied to analyze and interpret these data.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">The data showed a non-linear decline in PET measurements, which we modelled successfully by an exponential function depending on two patient-related covariates duration since symptom onset and age at symptom onset. We found that the degree of damage was significantly greater in the posterior putamen than in the anterior putamen throughout the disease. We also found that over the course of the illness, the difference between the less affected and more affected sides of the brain decreased in the anterior putamen. Younger patients had significantly poorer measurements than older patients at the time of symptom onset suggesting more effective compensatory mechanisms delaying the onset of symptoms. Cautious extrapolation showed that disease onset had occurred some 8 to 17 years prior to symptom onset.</AbstractText><AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">Our model provides important biological insights into the pathogenesis of PD, as well as its preclinical aspects. Our methodology can be applied widely to study many other chronic progressive diseases.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Kuramoto</LastName><ForeName>Lisa</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>Centre for Clinical Epidemiology & Evaluation, Vancouver Coastal Health Research Institute, Vancouver, British Columbia, Canada.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Cragg</LastName><ForeName>Jacquelyn</ForeName><Initials>J</Initials></Author><Author ValidYN="Y"><LastName>Nandhagopal</LastName><ForeName>Ramachandiran</ForeName><Initials>R</Initials></Author><Author ValidYN="Y"><LastName>Mak</LastName><ForeName>Edwin</ForeName><Initials>E</Initials></Author><Author ValidYN="Y"><LastName>Sossi</LastName><ForeName>Vesna</ForeName><Initials>V</Initials></Author><Author ValidYN="Y"><LastName>de la Fuente-Fernández</LastName><ForeName>Raul</ForeName><Initials>R</Initials></Author><Author ValidYN="Y"><LastName>Stoessl</LastName><ForeName>A Jon</ForeName><Initials>AJ</Initials></Author><Author ValidYN="Y"><LastName>Schulzer</LastName><ForeName>Michael</ForeName><Initials>M</Initials></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><Agency>Canadian Institutes of Health Research</Agency><Country>Canada</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2013</Year><Month>10</Month><Day>18</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>PLoS One</MedlineTA><NlmUniqueID>101285081</NlmUniqueID><ISSNLinking>1932-6203</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><CommentsCorrectionsList><CommentsCorrections RefType="Cites"><RefSource>Ann Neurol. 2000 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