Comparison of dyskinesia profiles after L-DOPA dose challenges with or without dopamine agonist coadministration

罗哌尼罗 运动亢进 运动障碍 左旋多巴 医学 肌张力障碍 多巴胺激动剂 藤架 多巴胺 心理学 帕金森病 兴奋剂 评定量表 麻醉 多巴胺能 药理学 内科学 精神科 疾病 发展心理学 受体
作者
Sotirios Grigoriou,Elena Espa,Per Odin,Jonathan Timpka,Gustaf von Grothusen,Andreas Jakobsson,M. Angela Cenci
出处
期刊:Neuropharmacology [Elsevier BV]
卷期号:237: 109630-109630 被引量:8
标识
DOI:10.1016/j.neuropharm.2023.109630
摘要

Many patients with Parkinson's disease (PD) experiencing l-DOPA-induced dyskinesia (LID) receive adjunct treatment with dopamine agonists, whose functional impact on LID is unknown. We set out to compare temporal and topographic profiles of abnormal involuntary movements (AIMs) after l-DOPA dose challenges including or not the dopamine agonist ropinirole. Twenty-five patients with PD and a history of dyskinesias were sequentially administered either l-DOPA alone (150% of usual morning dose) or an equipotent combination of l-DOPA and ropinirole in random order. Involuntary movements were assessed by two blinded raters prior and every 30 min after drug dosing using the Clinical Dyskinesia Rating Scale (CDRS). A sensor-recording smartphone was secured to the patients' abdomen during the test sessions. The two raters' CDRS scores were highly reliable and concordant with models of hyperkinesia presence and severity trained on accelerometer data. The dyskinesia time curves differed between treatments as the l-DOPA-ropinirole combination resulted in lower peak severity but longer duration of the AIMs compared with l-DOPA alone. At the peak of the AIMs curve (60-120 min), l-DOPA induced a significantly higher total hyperkinesia score, whereas in the end phase (240-270 min), both hyperkinesia and dystonia tended to be more severe after the l-DOPA-ropinirole combination (though reaching statistical significance only for the item, arm dystonia). Our results pave the way for the introduction of a combined l-DOPA-ropinirole challenge test in the early clinical evaluation of antidyskinetic treatments. Furthermore, we propose a machine-learning method to predict CDRS hyperkinesia severity using accelerometer data.

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