ORIGINAL RESEARCH article
Front. Neurol.
Sec. Dementia and Neurodegenerative Diseases
A Gut Microbiota-Based Predictive Model for Treatment Efficacy of Parkinson's Disease
Provisionally accepted
Chen Liu
Tianxia Yu*- Department of Neurology, Yantai Yantaishan Hospital, Yantai, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Objective: To develop and validate a predictive model for treatment efficacy decline in Parkinson's disease (PD) patients based on clinical characteristics and biological markers, providing a basis for early risk identification and personalized therapeutic strategies. Methods: A retrospective study of 500 PD patients admitted to our hospital between January 2021 and December 2024 was enrolled and randomly divided into a training set (n=350) and a validation set (n=150) at a 7:3 ratio. Demographic characteristics, clinical rating scales, and biological markers were collected. In the training set, univariate analysis was performed to screen variables associated with efficacy decline. After variable compression via LASSO regression, multivariate logistic regression was used to identify independent predictors. Random forest (RF), support vector machine (SVM), and gradient boosting models were constructed using Python 3.8.5 and the sklearn library. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC), and the optimal model was selected with key predictor importance analyzed. Results: No significant differences in baseline characteristics were observed between the training and validation sets (all P>0.05). Multivariate logistic regression identified the total MDS-UPDRS score, MDS-UPDRS II (activities of daily living), MDS-UPDRS IV (motor complications), PDQ-39 score, E. coli/Lactobacillus ratio, fecal lactoferrin, and fecal calprotectin as independent risk factors (all P<0.05), while total fecal bacterial count was an independent protective factor (all P<0.05). The RF model demonstrated superior predictive performance (AUC=0.874, 95%CI: 0.831-0.917) compared to SVM (AUC=0.806, 95%CI: 0.753-0.859) and gradient boosting (AUC=0.842, 95%CI: 0.794-0.889). Conclusion: The RF model incorporating clinical and biological markers effectively predicts treatment efficacy decline in PD patients, with fecal calprotectin, fecal lactoferrin, and the E. coli/Lactobacillus ratio serving as key predictors.
Keywords: Gut Microbiota, Parkinson's disease, Fecal calprotectin, Fecal lactoferrin, E. coli/Lactobacillus ratio
Received: 16 Aug 2025; Accepted: 02 Dec 2025.
Copyright: © 2025 Liu and Yu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Tianxia Yu
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.