Medical Physics Section, Department of Biomedicine and Prevention, University of Rome Tor Vergata, Via Montpellier, 1, 00133 Rome, Italy.
Department of Brain and Behavioral Sciences, IRCCS Mondino Foundation, National Neurological Institute, University of Pavia, Via Mondino 2, 27100 Pavia, Italy; Computational and Translational Neuroscience Laboratory, Institute of Cognitive Sciences and Technologies, National Research Council (CTNLab-ISTC-CNR), Via Gian Domenico Romagnosi, 18A, 00196 Rome, Italy.
J Neurol Sci. 2024 Jul 15;462:123091. doi: 10.1016/j.jns.2024.123091. Epub 2024 Jun 8.
Sex differences affect Parkinson's disease (PD) development and manifestation. Yet, current PD identification and treatments underuse these distinctions. Sex-focused PD literature often prioritizes prevalence rates over feature importance analysis. However, underlying aspects could make a feature significant for predicting PD, despite its score. Interactions between features require consideration, as do distinctions between scoring disparities and actual feature importance. For instance, a higher score in males for a certain feature doesn't necessarily mean it's less important for characterizing PD in females. This article proposes an explainable Machine Learning (ML) model to elucidate these underlying factors, emphasizing the importance of features. This insight could be critical for personalized medicine, suggesting the need to tailor data collection and analysis for males and females. The model identifies sex-specific differences in PD, aiding in predicting outcomes as "Healthy" or "Pathological". It adopts a system-level approach, integrating heterogeneous data - clinical, imaging, genetics, and demographics - to study new biomarkers for diagnosis. The explainable ML approach aids non-ML experts in understanding model decisions, fostering trust and facilitating interpretation of complex ML outcomes, thus enhancing usability and translational research. The ML model identifies muscle rigidity, autonomic and cognitive assessments, and family history as key contributors to PD diagnosis, with sex differences noted. The genetic variant SNCA-rs356181 may be more significant in characterizing PD in males. Interaction analysis reveals a greater occurrence of feature interplay among males compared to females. These disparities offer insights into PD pathophysiology and could guide the development of sex-specific diagnostic and therapeutic approaches.
性别差异影响帕金森病（PD）的发展和表现。然而，目前的 PD 识别和治疗方法对这些差异的利用不足。以性别为重点的 PD 文献通常优先考虑流行率，而不是对特征重要性进行分析。然而，潜在的方面可能会使一个特征对预测 PD 具有重要意义，尽管其得分较低。需要考虑特征之间的相互作用，以及评分差异和实际特征重要性之间的区别。例如，男性在某个特征上的得分较高并不一定意味着它对女性 PD 的特征描述不重要。本文提出了一个可解释的机器学习（ML）模型来阐明这些潜在因素，强调特征的重要性。这一见解对于个性化医疗可能至关重要，表明需要针对男性和女性量身定制数据收集和分析。该模型确定了 PD 中的性别特异性差异，有助于预测结果为“健康”或“病理”。它采用系统级方法，整合了异构数据 - 临床、影像、遗传和人口统计学 - 来研究新的诊断生物标志物。可解释的 ML 方法帮助非 ML 专家理解模型决策，增强信任，并促进对复杂 ML 结果的解释，从而提高可用性和转化研究。ML 模型确定肌肉僵硬、自主和认知评估以及家族史是 PD 诊断的关键因素，并注意到性别差异。遗传变异 SNCA-rs356181 可能在男性 PD 特征描述中更为重要。交互分析显示，男性特征之间的相互作用比女性更为频繁。这些差异提供了对 PD 病理生理学的深入了解，并可能指导开发针对特定性别的诊断和治疗方法。
