ArticleJournal of neurology2026
Balance biomarker for early differentiation of Parkinson's disease and multiple system atrophy with parkinsonian type.
Article in Journal of neurology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundParkinson's disease (PD) and multiple system atrophy with parkinsonian type (MSA-P) share various motor and nonmotor symptoms, complicating early differential diagnosis, although prognosis and levodopa response differ.
objectivesTo develop a machine learning model using balance analysis to differentiate early-stage PD and MSA-P.
methodsWe enrolled 22 healthy controls (HC), 20 PD, and 17 MSA-P patients within three years of onset. Participants stood for 60 s on dual force-plates under eyes open (EO) and eyes closed (EC). Seven center of pressure (COP) parameters per condition and EC-EO differences were analyzed. Group differences were assessed with ANOVA. Feature selection was performed using LightGBM (LGBM), followed by model training with fourfold cross-validation.
resultsMean ages were 63.8 (HC), 64.2 (PD), and 68.8 years (MSA-P). Disease durations were 2.4 years in PD and 1.8 years in MSA-P. Except for mean distance in the anteroposterior direction with EO and 95% confidence ellipse area with EC, all parameters showed significant group differences. Post-hoc analysis revealed significant differences between HC and PD/MSA-P in EO, whereas in EC, differences were more pronounced between MSA-P and PD/HC. The LGBM model achieved 81.4% accuracy in distinguishing PD from MSA-P, 94.9% in distinguishing healthy controls from patient groups, and 84.9% for three-group classification.
conclusionBalance biomarker, which analyzes the balance parameters with machine learning model could differentiate early-stage PD and MSA-P with high accuracy.
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