Dataset Information

Estimating the Evolution of Disease in the Parkinson's Progression Markers Initiative.

ABSTRACT: Parkinson's disease is the second most common neurological disease and affects about 1% of persons over the age of 60 years. Due to the lack of approved surrogate markers, confirmation of the disease still requires postmortem examination. Identifying and validating biomarkers are essential steps toward improving clinical diagnosis and accelerating the search for therapeutic drugs to ameliorate disease symptoms. Until recently, statistical analysis of multicohort longitudinal studies of neurodegenerative diseases has usually been restricted to a single analysis per outcome with simple comparisons between diagnostic groups. However, an important methodological consideration is to allow the modeling framework to handle multiple outcomes simultaneously and consider the transitions between diagnostic groups. This enables researchers to monitor multiple trajectories, correctly account for the correlation among biomarkers, and assess how these associations may jointly change over the long-term course of disease. In this study, we apply a latent time joint mixed-effects model to study biomarker progression and disease dynamics in the Parkinson's Progression Markers Initiative (PPMI) and examine which markers might be most informative in the earliest phases of disease. The results reveal that, even though diagnostic category was not included in the model, it seems to accurately reflect the temporal ordering of the disease state consistent with diagnosis categorization at baseline. In addition, results indicated that the specific binding ratio on striatum and the total Unified Parkinson's Disease Rating Scale (UPDRS) show high discriminability between disease stages. An extended latent time joint mixed-effects model with heterogeneous latent time variance also showed improvement in model fit in a simulation study and when applied to real data.

SUBMITTER: Iddi S

PROVIDER: S-EPMC6314496 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Estimating the Evolution of Disease in the Parkinson's Progression Markers Initiative.

Iddi Samuel S Li Dan D Aisen Paul S PS Rafii Michael S MS Litvan Irene I Thompson Wesley K WK Donohue Michael C MC

Neuro-degenerative diseases 20180808 4

Parkinson's disease is the second most common neurological disease and affects about 1% of persons over the age of 60 years. Due to the lack of approved surrogate markers, confirmation of the disease still requires postmortem examination. Identifying and validating biomarkers are essential steps toward improving clinical diagnosis and accelerating the search for therapeutic drugs to ameliorate disease symptoms. Until recently, statistical analysis of multicohort longitudinal studies of neurodege ...[more]

PMID: 30089306

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Project description:BackgroundIdentifying a meaningful progression metric for Parkinson's disease (PD) that reflects heterogeneity remains a challenge.ObjectiveTo assess the frequency and baseline predictors of progression to clinically relevant motor and non-motor PD milestones.MethodsUsing data from the Parkinson's Progression Markers Initiative (PPMI) de novo PD cohort, we monitored 25 milestones across six domains ("walking and balance"; "motor complications"; "cognition"; "autonomic dysfunction"; "functional dependence"; "activities of daily living"). Milestones were intended to be severe enough to reflect meaningful disability. We assessed the proportion of participants reaching any milestone; evaluated which occurred most frequently; and conducted a time-to-first-event analysis exploring whether baseline characteristics were associated with progression.ResultsHalf of participants reached at least one milestone within five years. Milestones within the cognitive, functional dependence, and autonomic dysfunction domains were reached most often. Among participants who reached a milestone at an annual follow-up visit and remained active in the study, 82% continued to meet criteria for any milestone at one or more subsequent annual visits and 55% did so at the next annual visit. In multivariable analysis, baseline features predicting faster time to reaching a milestone included age (p < 0.0001), greater MDS-UPDRS total scores (p < 0.0001), higher GDS-15 depression scores (p = 0.0341), lower dopamine transporter binding (p = 0.0043), and lower CSF total α-synuclein levels (p = 0.0030). Symptomatic treatment was not significantly associated with reaching a milestone (p = 0.1639).ConclusionClinically relevant milestones occur frequently, even in early PD. Milestones were significantly associated with baseline clinical and biological markers, but not with symptomatic treatment. Further studies are necessary to validate these results, further assess the stability of milestones, and explore translating them into an outcome measure suitable for observational and therapeutic studies.

Project description:Background: Orthostatic hypotension (OH) in Parkinson's disease (PD) can lead to falls, impair quality of life, and increase mortality. A trajectory analysis of OH could be useful to predict and prevent the hypotension incidence early. Methods: The longitudinal data of 660 patients with PD with disease duration up to 12 years were extracted from an integrated PD database. We used latent class mixed modeling (LCMM) to identify patient subgroups, demonstrating trajectories of changes in orthostatic blood pressure (BP) over time. The optimal number of subgroups was selected by several criteria including the Bayesian Information Criterion. Baseline information comparison between groups and backward stepwise logistic regression were conducted to define the distinguishing characteristics of these subgroups and to investigate the predictors for BP trajectory. Results: We identified three trajectories for each orthostatic change of systolic blood pressure (ΔSBP), namely, Class 1 (i.e., the increasing class) consisted of 18 participants with low ΔSBP that increased continuously during the follow-up; Class 2 (i.e., the low-stable class) consisted of 610 participants with low ΔSBP that remained low throughout the follow-up; and Class 3 (i.e., the high-stable class) consisted of 32 participants with high ΔSBP at baseline that was relatively stable throughout the follow-up. Several parameters differed among subgroups, but only male sex [odds ratio (OR) = 4.687, 95% confidence interval (CI) = 1.024-21.459], lower supine diastolic blood pressure (DBP) (OR = 0.934, 95% CI = 0.876-0.996), and lower level of total protein at baseline (OR = 0.812, 95% CI = 0.700-0.941) were significant predictors of an increasing ΔSBP trajectory. Conclusion: This study provides new information on the longitudinal development of ΔSBP in patients with PD with distinct trajectories of rapidly increasing, low-stable, and high-stable class. The parameters such as male sex, lower supine DBP, and lower total proteins help to identify the rapidly increasing class.

Dataset Information

Estimating the Evolution of Disease in the Parkinson's Progression Markers Initiative.

Publications

Estimating the Evolution of Disease in the Parkinson's Progression Markers Initiative.

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