Identifying clinical features and blood biomarkers associated with mild cognitive impairment in Parkinson disease using machine learning.

ABSTRACT

BACKGROUND AND PURPOSE: 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-MCI and (ii) to explore new blood biomarkers related to PD-MCI. METHODS: ShapleyVIC-assisted variable selection was used to identify a subset of variables from 41 variables potentially associated with PD-MCI in a cross-sectional study. Backward selection was used to further identify the variables associated with PD-MCI. Relative risk was used to quantify the association of final associated variables and PD-MCI in the final multivariable log-binomial regression model. RESULTS: Among 41 variables analysed, 22 variables were identified as significantly important variables associated with PD-MCI and eight variables were subsequently selected in the final model, indicating fewer years of education, shorter history of hypertension, higher Movement Disorder Society-Unified Parkinson's Disease Rating Scale motor score, higher levels of triglyceride (TG) and apolipoprotein A1 (ApoA1), and SNCA rs6826785 noncarrier status were associated with increased risk of PD-MCI (p < 0.05). CONCLUSIONS: Our study highlighted the strong association between TG, ApoA1, SNCA rs6826785, and PD-MCI by machine learning approach. Screening and management of high TG and ApoA1 levels might help prevent cognitive impairment in early PD patients. SNCA rs6826785 could be a novel therapeutic target for PD-MCI. ShapleyVIC-assisted variable selection is a novel and robust alternative to traditional approaches for future clinical study to prioritize the variables of interest.