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Beyond motor fluctuations: Understanding the clinical correlates of OFF burden in Parkinson's disease
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DOI：10.1177/1877718x261442857.png)
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Abstract
En 中文
<jats:sec>
<jats:title>Background</jats:title>
<jats:p>Historically, OFF burden in Parkinson's disease has been primarily attributed to motor features. Recent studies highlight that non-motor symptoms, and the predictability of OFF episodes also drive functional impairment, yet they are rarely measured in clinical practice.</jats:p>
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<jats:sec>
<jats:title>Objective</jats:title>
<jats:p>To identify which clinical features are most closely associated with OFF time and OFF impact, and to quantify the added explanatory value of temporal predictability, non-motor, and behavioural domains beyond a core motor model.</jats:p>
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<jats:title>Methods</jats:title>
<jats:p>We analysed 1252 OFF-only visits from 430 PPMI participants. Outcomes were MDS-UPDRS IV 4.3 (OFF time) and 4.4 (OFF impact). Linear mixed-effects models with a participant random intercept were fitted. The core motor model included OFF-state motor severity, freezing, tremor, levodopa responsiveness, and dyskinesia, plus covariates. Predictability (IV 4.5), non-motor (mood, fatigue/sleep, autonomic/GI), and behavioural (impulse-control behaviours) domains were then added to assess added influence beyond motor. Analyses were stratified by time since diagnosis (Pooled; ≤ 4 y; ≥ 6 y).</jats:p>
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<jats:title>Results</jats:title>
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Clinical features explained more variance in OFF impact than OFF time (25.9% vs 8.1%). OFF time was primarily linked to OFF-state motor severity/freezing, with levodopa responsiveness important early. For OFF impact, predictability produced the largest increment in marginal R
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beyond the core motor model (pooled and Late). Within the core motor model, tremor was the largest contributor to OFF impact.
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<jats:title>Conclusions</jats:title>
<jats:p>Predictability is a prominent correlate of OFF impact. Asking about predictability may help tailor therapy, from timing optimisation to on-demand rescue for unpredictable episodes.</jats:p>
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