Aborageh Mohamed, Hähnel Tom, Martins Conde Patricia, Klucken Jochen, Fröhlich Holger
Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757, Sankt, Augustin, Germany.
Department of Neurology, University Hospital and Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
NPJ Parkinsons Dis. 2025 May 13;11(1):126. doi: 10.1038/s41531-025-00983-4.
Parkinson's disease (PD) exhibits a variety of symptoms, with approximately 25% of patients experiencing mild cognitive impairment and 45% developing dementia within ten years of diagnosis. Predicting this progression and identifying its causes remains challenging. Our study utilizes machine learning and multimodal data from the UK Biobank to explore the predictability of Parkinson's dementia (PDD) post-diagnosis, further validated by data from the Parkinson's Progression Markers Initiative (PPMI) cohort. Using Shapley Additive Explanation (SHAP) and Bayesian Network structure learning, we analyzed interactions among genetic predisposition, comorbidities, lifestyle, and environmental factors. We concluded that genetic predisposition is the dominant factor, with significant influence from comorbidities. Additionally, we employed Mendelian randomization (MR) to establish potential causal links between hypertension, type 2 diabetes, and PDD, suggesting that managing blood pressure and glucose levels in Parkinson's patients may serve as a preventive strategy. This study identifies risk factors for PDD and proposes avenues for prevention.
帕金森病（PD）表现出多种症状，约25%的患者会出现轻度认知障碍，45%的患者在确诊后十年内会发展为痴呆。预测这种病情进展并确定其原因仍然具有挑战性。我们的研究利用来自英国生物银行的机器学习和多模态数据，探讨帕金森病痴呆（PDD）诊断后的可预测性，并通过帕金森病进展标志物倡议（PPMI）队列的数据进行进一步验证。我们使用夏普利加性解释（SHAP）和贝叶斯网络结构学习，分析了遗传易感性、合并症、生活方式和环境因素之间的相互作用。我们得出结论，遗传易感性是主导因素，合并症也有显著影响。此外，我们采用孟德尔随机化（MR）来建立高血压、2型糖尿病与PDD之间的潜在因果联系，这表明控制帕金森病患者的血压和血糖水平可能是一种预防策略。本研究确定了PDD的风险因素并提出了预防途径。
