{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T16:46:26Z","timestamp":1791996386884,"version":"4.4.0"},"reference-count":31,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2023,3,26]],"date-time":"2023-03-26T00:00:00Z","timestamp":1679788800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Euro J of Neurology"],"published-print":{"date-parts":[[2023,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background and purpose<\/jats:title><jats:p>A broad list of variables associated with mild cognitive impairment (MCI) in Parkinson disease (PD) have been investigated separately. However, there is as yet no study including all of them to assess variable importance. Shapley variable importance cloud (ShapleyVIC) can robustly assess variable importance while accounting for correlation between variables. Objectives of this study were (i) to prioritize the important variables associated with PD\u2010MCI and (ii) to explore new blood biomarkers related to PD\u2010MCI.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>ShapleyVIC\u2010assisted variable selection was used to identify a subset of variables from 41 variables potentially associated with PD\u2010MCI in a cross\u2010sectional study. Backward selection was used to further identify the variables associated with PD\u2010MCI. Relative risk was used to quantify the association of final associated variables and PD\u2010MCI in the final multivariable log\u2010binomial regression model.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Among 41 variables analysed, 22 variables were identified as significantly important variables associated with PD\u2010MCI and eight variables were subsequently selected in the final model, indicating fewer years of education, shorter history of hypertension, higher Movement Disorder Society\u2013Unified Parkinson's Disease Rating Scale motor score, higher levels of triglyceride (TG) and apolipoprotein A1 (ApoA1), and <jats:italic>SNCA<\/jats:italic> rs6826785 noncarrier status were associated with increased risk of PD\u2010MCI (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.05).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Our study highlighted the strong association between TG, ApoA1, <jats:italic>SNCA<\/jats:italic> rs6826785, and PD\u2010MCI by machine learning approach. Screening and management of high TG and ApoA1 levels might help prevent cognitive impairment in early PD patients. <jats:italic>SNCA<\/jats:italic> rs6826785 could be a novel therapeutic target for PD\u2010MCI. ShapleyVIC\u2010assisted variable selection is a novel and robust alternative to traditional approaches for future clinical study to prioritize the variables of interest.<\/jats:p><\/jats:sec>","DOI":"10.1111\/ene.15785","type":"journal-article","created":{"date-parts":[[2023,3,13]],"date-time":"2023-03-13T11:57:25Z","timestamp":1678708645000},"page":"1658-1666","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Identifying clinical features and blood biomarkers associated with mild cognitive impairment in Parkinson disease using machine learning"],"prefix":"10.1111","volume":"30","author":[{"given":"Xiao","family":"Deng","sequence":"first","affiliation":[{"name":"Department of Neurology National Neuroscience Institute  Singapore City Singapore"},{"name":"Duke\u2010NUS Medical School  Singapore City 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