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用于预测帕金森病进展速度的基因表达分类器的创建。

Creation of a gene expression classifier for predicting Parkinson's disease rate of progression.

机构信息

BioShai Ltd., 1 Ha-Tsmikha St., Yokneam, Israel.

Sackler School of Medicine, Tel Aviv University, 6997801, Tel Aviv, Israel.

出版信息

J Neural Transm (Vienna). 2020 May;127(5):755-762. doi: 10.1007/s00702-020-02194-y. Epub 2020 May 8.

DOI:10.1007/s00702-020-02194-y
PMID:32385576
Abstract

Parkinson's disease (PD) etiology is heterogeneous, genetic, and multi-factorial, resulting in a varied disease from a mild slow progression to a more severe rapid progression. Prognostic information on the nature of the patient's disease at diagnosis aids the physician in counseling patients on treatment options and life planning. In a cohort of PD patients from the PPMI study, the relative gene expression levels of SKP1A, UBE2K, ALDH1A1, PSMC4, HSPA8 and LAMB2 were measured in baseline blood samples by real-time quantitative PCR. At baseline PD patients were up to 2 years from diagnosis, H&Y scale ≤ 2 and PD treatment naïve. PD-Prediction algorithm comprised of ALDH1A1, LAMB2, UBE2K, SKP1A and age was created by logistic regression for predicting progression to ≤ 70% Modified Schwab and England Activities of Daily Living (S&E-ADL). In relation to patients negative for PD-Prediction (n = 180), patients positive (n = 30) for Cutoff-1 (at 82% specificity, 80.0% sensitivity) had positive hazard ratio (HR+) of 10.6 (95% CI, 2.2-50.1), and positive (n = 23) for Cutoff-2 (at 93% specificity, 47% sensitivity) had HR+ of 17.1 (95% CI, 3.2-89.9) to progress to ≤ 70% S&E-ADL within 3 years (P value < 0.0001). Likewise, patients positive for PD-Prediction Cutoff-1 (n = 49) had HR+ 4.3 (95% CI, 1.6-11.6) for faster time to H&Y 3 in relation to patients negative (n = 170) for PD-Prediction (P value = 0.0002). Our findings show an algorithm that seems to predict fast PD progression and may potentially be used as a tool to assist the physician in choosing an optimal treatment plan, improving the patient's quality of life and overall health outcome.

摘要

帕金森病 (PD) 的病因具有异质性,涉及遗传和多种因素,导致疾病表现从轻度缓慢进展到更严重的快速进展。在诊断时,患者疾病性质的预后信息可以帮助医生为患者提供治疗选择和生活规划方面的建议。在 PPMI 研究的 PD 患者队列中,通过实时定量 PCR 测量了基线血液样本中 SKP1A、UBE2K、ALDH1A1、PSMC4、HSPA8 和 LAMB2 的相对基因表达水平。在基线时,PD 患者的诊断时间不超过 2 年,H&Y 量表≤2,且未接受 PD 治疗。通过逻辑回归创建了一个由 ALDH1A1、LAMB2、UBE2K、SKP1A 和年龄组成的 PD 预测算法,用于预测进展至≤70%改良 Schwab 和 England 日常生活活动量表(S&E-ADL)。与 PD 预测阴性的患者(n=180)相比,PD 预测阳性(n=30)的患者(特异性为 82%,敏感性为 80.0%)的阳性危险比(HR+)为 10.6(95%CI,2.2-50.1),PD 预测阳性(n=23)的患者(特异性为 93%,敏感性为 47%)的 HR+为 17.1(95%CI,3.2-89.9),在 3 年内进展至≤70%的 S&E-ADL(P 值<0.0001)。同样,与 PD 预测阴性的患者(n=170)相比,PD 预测阳性的患者(n=49)的 HR+为 4.3(95%CI,1.6-11.6),提示他们的 H&Y 量表更快进展至 3(P 值=0.0002)。我们的研究结果表明,存在一种似乎可以预测 PD 快速进展的算法,它可能作为一种工具,帮助医生选择最佳治疗方案,提高患者的生活质量和整体健康结局。

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