Predicting Progression in Parkinson's Disease Using Baseline and 1-Year Change Measures.


Journal

Journal of Parkinson's disease
ISSN: 1877-718X
Titre abrégé: J Parkinsons Dis
Pays: Netherlands
ID NLM: 101567362

Informations de publication

Date de publication:
2019
Historique:
pubmed: 28 8 2019
medline: 30 7 2020
entrez: 28 8 2019
Statut: ppublish

Résumé

Improved prediction of Parkinson's disease (PD) progression is needed to support clinical decision-making and to accelerate research trials. To examine whether baseline measures and their 1-year change predict longer-term progression in early PD. Parkinson's Progression Markers Initiative study data were used. Participants had disease duration ≤2 years, abnormal dopamine transporter (DAT) imaging, and were untreated with PD medications. Baseline and 1-year change in clinical, cerebrospinal fluid (CSF), and imaging measures were evaluated as candidate predictors of longer-term (up to 5 years) change in Movement Disorders Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) score and DAT specific binding ratios (SBR) using linear mixed-effects models. Among 413 PD participants, median follow-up was 5 years. Change in MDS-UPDRS from year-2 to last follow-up was associated with disease duration (β= 0.351; 95% CI = 0.146, 0.555), male gender (β= 3.090; 95% CI = 0.310, 5.869), and baseline (β= -0.199; 95% CI = -0.315, -0.082) and 1-year change (β= 0.540; 95% CI = 0.423, 0.658) in MDS-UPDRS; predictors in the model accounted for 17.6% of the variance in outcome. Predictors of percent change in mean SBR from year-2 to last follow-up included baseline rapid eye movement sleep behavior disorder score (β= -0.6229; 95% CI = -1.2910, 0.0452), baseline (β= 7.232; 95% CI = 2.268, 12.195) and 1-year change (β= 45.918; 95% CI = 35.994,55.843) in mean striatum SBR, and 1-year change in autonomic symptom score (β= -0.325;95% CI = -0.695, 0.045); predictors in the model accounted for 44.1% of the variance. Baseline clinical, CSF, and imaging measures in early PD predicted change in MDS-UPDRS and dopamine-transporter binding, but the predictive value of the models was low. Adding the short-term change of possible predictors improved the predictive value, especially for modeling change in dopamine-transporter binding.

Sections du résumé

BACKGROUND
Improved prediction of Parkinson's disease (PD) progression is needed to support clinical decision-making and to accelerate research trials.
OBJECTIVES
To examine whether baseline measures and their 1-year change predict longer-term progression in early PD.
METHODS
Parkinson's Progression Markers Initiative study data were used. Participants had disease duration ≤2 years, abnormal dopamine transporter (DAT) imaging, and were untreated with PD medications. Baseline and 1-year change in clinical, cerebrospinal fluid (CSF), and imaging measures were evaluated as candidate predictors of longer-term (up to 5 years) change in Movement Disorders Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) score and DAT specific binding ratios (SBR) using linear mixed-effects models.
RESULTS
Among 413 PD participants, median follow-up was 5 years. Change in MDS-UPDRS from year-2 to last follow-up was associated with disease duration (β= 0.351; 95% CI = 0.146, 0.555), male gender (β= 3.090; 95% CI = 0.310, 5.869), and baseline (β= -0.199; 95% CI = -0.315, -0.082) and 1-year change (β= 0.540; 95% CI = 0.423, 0.658) in MDS-UPDRS; predictors in the model accounted for 17.6% of the variance in outcome. Predictors of percent change in mean SBR from year-2 to last follow-up included baseline rapid eye movement sleep behavior disorder score (β= -0.6229; 95% CI = -1.2910, 0.0452), baseline (β= 7.232; 95% CI = 2.268, 12.195) and 1-year change (β= 45.918; 95% CI = 35.994,55.843) in mean striatum SBR, and 1-year change in autonomic symptom score (β= -0.325;95% CI = -0.695, 0.045); predictors in the model accounted for 44.1% of the variance.
CONCLUSIONS
Baseline clinical, CSF, and imaging measures in early PD predicted change in MDS-UPDRS and dopamine-transporter binding, but the predictive value of the models was low. Adding the short-term change of possible predictors improved the predictive value, especially for modeling change in dopamine-transporter binding.

Identifiants

pubmed: 31450510
pii: JPD181518
doi: 10.3233/JPD-181518
pmc: PMC6839498
doi:

Substances chimiques

Biomarkers 0
Dopamine Plasma Membrane Transport Proteins 0

Types de publication

Journal Article Multicenter Study Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

665-679

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Auteurs

Lana M Chahine (LM)

Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA.

Andrew Siderowf (A)

Departments of Neurology Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

Janel Barnes (J)

Department of Biostatistics, College of Public Health, University of Iowa, Iowa City, IA, USA.

Nicholas Seedorff (N)

Department of Biostatistics, College of Public Health, University of Iowa, Iowa City, IA, USA.

Chelsea Caspell-Garcia (C)

Department of Biostatistics, College of Public Health, University of Iowa, Iowa City, IA, USA.

Tanya Simuni (T)

Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.

Christopher S Coffey (CS)

Department of Biostatistics, College of Public Health, University of Iowa, Iowa City, IA, USA.

Douglas Galasko (D)

Department of Neurology, University of California, San Diego, CA, USA.

Brit Mollenhauer (B)

Department of Neurology, University Medical Center Goettingen, Goettingen, Germany and Paracelsus-Elena-Klinik, Kassel, Germany.

Vanessa Arnedo (V)

The Michael J. Fox Foundation, New York, NY, USA.

Nichole Daegele (N)

Institute for Neurodegenerative Disorders, New Haven, CT, USA.

Mark Frasier (M)

The Michael J. Fox Foundation, New York, NY, USA.

Caroline Tanner (C)

Department of Neurology, University of California San Francisco, San Francisco, CA, USA.

Karl Kieburtz (K)

Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA.

Kenneth Marek (K)

Institute for Neurodegenerative Disorders, New Haven, CT, USA.

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Classifications MeSH