There is an urgent need to identify predictive biomarkers of Parkinson’s disease (PD) with cognitive impairment (PDCI) in order to individualize patient management, ensure timely intervention, and improve prognosis. The aim of this study was to screen for these biomarkers by comparing the plasma proteome and metabolome of PD patients with or without cognitive impairment. Proteomics and metabolomics analyses were performed on a discover cohort. A machine learning model was used to identify candidate protein and metabolite biomarkers of PDCI, which were validated in an independent cohort. The predictive ability of these biomarkers for PDCI was evaluated by plotting receiver operating characteristic curves and calculating the area under the curve (AUC). Moreover, we assessed the predictive ability of these proteins in combination with neuroimaging. In the discover cohort (n = 100), we identified 25 protein features with best results in the machine learning model, including top-ranked PSAP and H3C15. The two-proteins were used for model construction, achieving an Area under the curve (AUC) of 0.951 in the train set and AUC of 0.981 in the test set. Similarly, the model gives a rank list of endogenous metabolite features, Glycocholic Acid and 6-Methylnicotinamide were two top features. Combining these two markers further got the AUC of 0.969 in train set and 0.867 in the test set. To validate the performance of the protein biomarkers, we performed targeted analysis of selected proteins (H3C15 and PSAP) and proteins likely associated with PDCI (NCAM2 and LAMB2) using parallel reaction monitoring in validation cohort (n = 116). The AUC of the classifier built with H3C15 and PSAP is 0.813. Moreover, when combining H3C15, PSAP, NCAM2, and LAMB2, the model achieved AUC of 0.983 in the train set, AUC of 0.981 in the test set, and AUC of 0.839 in the validation set. Furthermore, we verified that these protein markers we discovered can improve the predictive effect of neuroimaging on PDCI: the classifier built with neuroimaging features had AUC of 0.833, which improved to 0.905 when combined with H3C15. Taken together, our integrated proteomics and metabolomics analysis successfully identified potential biomarkers for PDCI. Additionally, H3C15 showed promise in enhancing the predictive performance of neuroimaging for cognitive impairment.

迫切需要确定帕金森病 （PD） 伴认知障碍 （PDCI） 的预测生物标志物，以便个体化患者管理、确保及时干预并改善预后。本研究的目的是通过比较有或没有认知障碍的 PD 患者的血浆蛋白质组和代谢组来筛选这些生物标志物。对发现队列进行蛋白质组学和代谢组学分析。使用机器学习模型来识别 PDCI 的候选蛋白质和代谢物生物标志物，并在独立队列中进行了验证。通过绘制受试者工作特征曲线并计算曲线下面积 （AUC） 来评估这些生物标志物对 PDCI 的预测能力。此外，我们结合神经影像学评估了这些蛋白质的预测能力。在发现队列 （n = 100） 中，我们确定了 25 个在机器学习模型中效果最好的蛋白质特征，包括排名靠前的 PSAP 和 H3C15。这两种蛋白质用于模型构建，在训练集中实现 0.951 的曲线下面积 （AUC），在测试集中实现 0.981 的 AUC。同样，该模型给出了内源性代谢物特征的排名列表，糖胆酸和 6-甲基烟酰胺是两个主要特征。进一步结合这两个标记，在训练集中得到 AUC 为 0.969，在测试集中为 0.867。为了验证蛋白质生物标志物的性能，我们在验证队列 （n = 116） 中使用平行反应监测对选定的蛋白质 （H3C15 和 PSAP） 和可能与 PDCI 相关的蛋白质 （NCAM2 和 LAMB2） 进行了靶向分析。使用 H3C15 和 PSAP 构建的分类器的 AUC 为 0.813。 此外，当结合 H3C15、PSAP、NCAM2 和 LAMB2 时，该模型在训练集中实现了 0.983 的 AUC，在测试集中实现了 0.981 的 AUC，在验证集中实现了 0.839 的 AUC。此外，我们验证了我们发现的这些蛋白质标志物可以提高神经影像学对 PDCI 的预测效果：使用神经影像学特征构建的分类器的 AUC 为 0.833，与 H3C15 结合时提高到 0.905。综上所述，我们的综合蛋白质组学和代谢组学分析成功确定了 PDCI 的潜在生物标志物。此外，H3C15 在增强神经影像学对认知障碍的预测性能方面显示出前景。