Parkinson's progression prediction using machine learning and serum cytokines
Parkinson's progression prediction using machine learning and serum cytokines
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DOI：
10.1038/s41531-019-0086-4
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发表时间：
2019-07-25
影响因子：
8.7
通讯作者：
Dzamko, Nicolas
中科院分区：
文献类型：
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作者：
Rastegar, Diba Ahmadi;Ho, Nicholas;Dzamko, Nicolas
帕金森病 (PD) 症状的异质性及其进展的变异性使患者治疗和临床试验的解释变得复杂。因此，人们对开发可以预测 PD 进展的模型很感兴趣。在这项研究中，我们使用了来自迈克尔·J·福克斯基金会 (Michael J Fox Foundation) 的长期跟踪临床特征的 PD 患者队列的血清样本，这些患者有或没有常见的富含亮氨酸重复激酶 2 (LRRK2) G2019S 突变。我们在基线和 1 年后测量了血清中的 27 种炎症细胞因子和趋化因子，以研究细胞因子的稳定性。然后，我们将基线测量与机器学习模型结合使用来预测 2 年后随访的纵向临床结果。使用归一化均方根误差 (NRMSE) 作为性能衡量标准，最佳预测模型是运动症状严重程度量表，Hoehn 和 Yahr 量表的 NRMSE 为 0.1123，统一帕金森病评定量表第三部分 (UPDRS III) 的 NRMSE 为 0.1193。对于每个模型，确定了对预测有贡献的主要变量，其中趋化因子巨噬细胞炎症蛋白 1 α (MIP1 α) 和单核细胞趋化蛋白 1 (MCP1) 分别对 Hoehn 和 Yahr 以及 UPDRS III 的预测做出最大的外周贡献。这些结果提供了关于 PD 外周炎症细胞因子的纵向评估的信息，并证明外周细胞因子可能有助于使用机器学习模型预测 PD 进展。
The heterogeneous nature of Parkinson's disease (PD) symptoms and variability in their progression complicates patient treatment and interpretation of clinical trials. Consequently, there is much interest in developing models that can predict PD progression. In this study we have used serum samples from a clinically well characterized longitudinally followed Michael J Fox Foundation cohort of PD patients with and without the common leucine-rich repeat kinase 2 (LRRK2) G2019S mutation. We have measured 27 inflammatory cytokines and chemokines in serum at baseline and after 1 year to investigate cytokine stability. We then used the baseline measurements in conjunction with machine learning models to predict longitudinal clinical outcomes after 2 years follow up. Using the normalized root mean square error (NRMSE) as a measure of performance, the best prediction models were for the motor symptom severity scales, with NRMSE of 0.1123 for the Hoehn and Yahr scale and 0.1193 for the unified Parkinson's disease rating scale part three (UPDRS III). For each model, the top variables contributing to prediction were identified, with the chemokines macrophage inflammatory protein one alpha (MIP1 alpha), and monocyte chemoattractant protein one (MCP1) making the biggest peripheral contribution to prediction of Hoehn and Yahr and UPDRS III, respectively. These results provide information on the longitudinal assessment of peripheral inflammatory cytokines in PD and give evidence that peripheral cytokines may have utility for aiding prediction of PD progression using machine learning models.