Identifying biological factors which contribute to the clinical progression of heterogeneous motor and non-motor phenotypes in Parkinson’s disease may help to better understand the disease process. Several lipid-related genetic risk factors for Parkinson’s disease have been identified, and the serum lipid signature of Parkinson’s disease patients is significantly distinguishable from controls. However, the extent to which lipid profiles are associated with clinical outcomes remains unclear. Untargeted high-performance liquid chromatography-tandem mass spectrometry identified >900 serum lipids in Parkinson’s disease subjects at baseline (n = 122), and the potential for machine learning models using these lipids to predict motor and non-motor clinical scores after 2 years (n = 67) was assessed. Machine learning models performed best when baseline serum lipids were used to predict the 2-year future Unified Parkinson’s disease rating scale part three (UPDRS III) and Geriatric Depression Scale scores (both normalised root mean square error = 0.7). Feature analysis of machine learning models indicated that species of lysophosphatidylethanolamine, phosphatidylcholine, platelet-activating factor, sphingomyelin, diacylglycerol and triacylglycerol were top predictors of both motor and non-motor scores. Serum lipids were overall more important predictors of clinical outcomes than subject sex, age and mutation status of the Parkinson’s disease risk gene LRRK2. Furthermore, lipids were found to better predict clinical scales than a panel of 27 serum cytokines previously measured in this cohort (The Michael J. Fox Foundation LRRK2 Clinical Cohort Consortium). These results suggest that lipid changes may be associated with clinical phenotypes in Parkinson’s disease.

识别导致帕金森病异质运动和非运动表型临床进展的生物学因素可能有助于更好地了解疾病过程。帕金森病的几种与脂质相关的遗传风险因素已被确定，帕金森病患者的血清脂质特征与对照组有显着区别。然而，血脂谱与临床结果的关联程度仍不清楚。非靶向高效液相色谱-串联质谱法在帕金森病受试者的基线 ( n = 122) 中确定了 >900 种血清脂质，以及使用这些脂质的机器学习模型在 2 年后预测运动和非运动临床评分的潜力 ( n = 67) 进行了评估。当使用基线血清脂质来预测 2 年未来统一帕金森病评分量表第三部分 (UPDRS III) 和老年抑郁量表评分（均归一化均方根误差 = 0.7）时，机器学习模型表现最佳。机器学习模型的特征分析表明，溶血磷脂酰乙醇胺、磷脂酰胆碱、血小板激活因子、鞘磷脂、二酰甘油和三酰甘油的种类是运动和非运动评分的首要预测因子。总体而言，血清脂质是比受试者性别、年龄和帕金森病风险基因LRRK2突变状态更重要的临床结果预测因子。此外，与之前在该队列（Michael J. Fox 基金会 LRRK2 临床队列联盟）中测量的一组 27 种血清细胞因子相比，脂质可以更好地预测临床规模。 这些结果表明脂质变化可能与帕金森病的临床表型有关。