{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:33:29Z","timestamp":1787027609523,"version":"build-2736575974"},"reference-count":38,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T00:00:00Z","timestamp":1774483200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>The accurate prediction of motor progression in Parkinson\u2019s disease (PD) remains a major clinical challenge that limits personalized treatment planning and efficient clinical trial design. In this study, we developed and validated a machine-learning framework integrating a targeted panel of plasma proteins measured by Olink proximity extension assays with clinical variables to stratify patients according to their progression risk. We analyzed baseline plasma samples from 211 early-stage PD patients enrolled in the Parkinson\u2019s Progression Markers Initiative (PPMI) cohort using four targeted Olink panels, from which 28 circulating proteins were retained after quality-control filtering. Patients were classified as rapid or slow progressors based on their annualized change in MDS-UPDRS Part III scores. Among the algorithms tested, Random Forest achieved the highest discriminative performance with an area under the receiver operating characteristic curve (AUC) of 0.751 (95% CI: 0.684\u20130.811), which exceeded that of clinical predictors alone (AUC 0.666). The integration of targeted proteomic and clinical features further improved model performance (AUC 0.773; p = 0.009). Nested cross-validation confirmed minimal optimistic bias (AUC 0.743). To enhance clinical interpretability, we applied SHapley Additive exPlanations (SHAP) analysis, which identified interleukin-6 (IL-6), brain-derived neurotrophic factor (BDNF), and vascular endothelial growth factor A (VEGF-A) as the most influential predictors. SHAP feature rankings were highly stable across cross-validation folds (mean Spearman \u03c1 = 0.91). The robustness of these findings was confirmed through sensitivity analyses using extreme quartile comparisons (AUC 0.823), treatment-na\u00efve subgroup analysis (AUC 0.738), and a clinically anchored outcome definition based on the minimal clinically important difference (AUC 0.739). A decision curve analysis demonstrated a net clinical benefit across threshold probabilities of 0.25\u20130.70. Our results establish targeted plasma protein profiling combined with interpretable machine learning as a promising tool for PD motor progression risk stratification, with potential applications in individualized patient counseling regarding motor prognosis and the selection of candidates for disease-modifying trials.<\/jats:p>","DOI":"10.3390\/bioengineering13040380","type":"journal-article","created":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T13:37:15Z","timestamp":1774532235000},"page":"380","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Machine-Learning-Based Targeted Plasma Proteomic Analysis for Predicting Motor Progression in Parkinson\u2019s Disease: An Interpretable Approach to Personalized Disease Management"],"prefix":"10.3390","volume":"13","author":[{"given":"Wei","family":"Lin","sequence":"first","affiliation":[{"name":"Department of Neurosurgery, The 904th Hospital of the Joint Logistics Support Force of PLA, Wuxi 214044, China"},{"name":"Department of Neurologic Surgery, Mayo Clinic Florida, Jacksonville, FL 32224, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sanjeet S.","family":"Grewal","sequence":"additional","affiliation":[{"name":"Department of Neurologic Surgery, Mayo Clinic Florida, Jacksonville, FL 32224, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2284","DOI":"10.1016\/S0140-6736(21)00218-X","article-title":"Parkinson\u2019s disease","volume":"397","author":"Bloem","year":"2021","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"896","DOI":"10.1016\/S0140-6736(14)61393-3","article-title":"Parkinson\u2019s disease","volume":"386","author":"Kalia","year":"2015","journal-title":"Lancet"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/S1474-4422(23)00405-2","article-title":"A biological definition of neuronal alpha-synuclein disease: Towards an integrated staging system for research","volume":"23","author":"Adler","year":"2024","journal-title":"Lancet Neurol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1002\/mds.28310","article-title":"A New Approach to the Development of Disease-Modifying Therapies for PD; Fighting Another Pandemic","volume":"36","author":"Kieburtz","year":"2021","journal-title":"Mov. 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