A molecular signature in blood identifies early Parkinson's disease.
A molecular signature in blood identifies early Parkinson's disease.
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DOI：
10.1186/1750-1326-7-26
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发表时间：
2012-05-31
影响因子：
15.1
通讯作者：
Mandel SA
中科院分区：
文献类型：
--
作者：
Molochnikov L;Rabey JM;Dobronevsky E;Bonucelli U;Ceravolo R;Frosini D;Grünblatt E;Riederer P;Jacob C;Aharon-Peretz J;Bashenko Y;Youdim MB;Mandel SA
在帕金森病（PD）中寻找生物标志物对于早期识别疾病以及监测神经保护疗法的有效性至关重要。我们的目的是评估在早期/轻度帕金森病患者的血液中是否能检测到一种基因特征，以支持早期帕金森病的诊断，重点关注在散发性帕金森病黑质中发现的特别改变的基因。
对来自62例早期帕金森病患者和64例年龄匹配的健康对照者的血液样本中7个选定基因的转录表达进行了检测。逐步多元逻辑回归分析确定了5个基因作为帕金森病的最佳预测因子：p19 S期激酶相关蛋白1A（优势比[OR]为0.73；95%置信区间[CI]为0.60 - 0.90）、亨廷顿蛋白相互作用蛋白 - 2（OR为1.32；CI为1.08 - 1.61）、醛脱氢酶家族1亚家族A1（OR为0.86；95% CI为0.75 - 0.99）、19S蛋白酶体蛋白PSMC4（OR为0.73；95% CI为0.60 - 0.89）以及热休克70 - kDa蛋白8（OR为1.39；95% CI为1.14 - 1.70）。在截断值为0.5时，该基因组在检测帕金森病时的灵敏度和特异性分别为90.3和89.1，接受者操作特征曲线下面积（ROC AUC）为0.96。
仅对构成早期帕金森病队列的初发帕金森病个体（n = 38）进行的五基因分类器的性能评估，得到了相似的ROC，AUC为0.95，表明该模型的稳定性，同时也表明患者用药对分类器预测帕金森病风险的概率（PP）没有显著影响。该模型的预测能力在一个由30例晚期帕金森病患者组成的独立队列中得到验证，将所有病例正确分类为帕金森病（100%灵敏度）。值得注意的是，该队列中帕金森病的预测概率名义平均值（0.95（标准差 = 0.09））高于早期帕金森病组（0.83（标准差 = 0.22）），这表明该模型有评估疾病严重程度的潜力。最后，该基因组完全区分了帕金森病和阿尔茨海默病（n = 29）。
这些研究结果证明了一个五基因组合诊断早期/轻度帕金森病的能力，对于在疾病明显表现之前检测无症状帕金森病可能具有诊断价值。
The search for biomarkers in Parkinson’s disease (PD) is crucial to identify the disease early and monitor the effectiveness of neuroprotective therapies. We aim to assess whether a gene signature could be detected in blood from early/mild PD patients that could support the diagnosis of early PD, focusing on genes found particularly altered in the substantia nigra of sporadic PD. The transcriptional expression of seven selected genes was examined in blood samples from 62 early stage PD patients and 64 healthy age-matched controls. Stepwise multivariate logistic regression analysis identified five genes as optimal predictors of PD: p19 S-phase kinase-associated protein 1A (odds ratio [OR] 0.73; 95% confidence interval [CI] 0.60–0.90), huntingtin interacting protein-2 (OR 1.32; CI 1.08–1.61), aldehyde dehydrogenase family 1 subfamily A1 (OR 0.86; 95% CI 0.75–0.99), 19 S proteasomal protein PSMC4 (OR 0.73; 95% CI 0.60–0.89) and heat shock 70-kDa protein 8 (OR 1.39; 95% CI 1.14–1.70). At a 0.5 cut-off the gene panel yielded a sensitivity and specificity in detecting PD of 90.3 and 89.1 respectively and the area under the receiving operating curve (ROC AUC) was 0.96. The performance of the five-gene classifier on the de novo PD individuals alone composing the early PD cohort (n = 38), resulted in a similar ROC with an AUC of 0.95, indicating the stability of the model and also, that patient medication had no significant effect on the predictive probability (PP) of the classifier for PD risk. The predictive ability of the model was validated in an independent cohort of 30 patients at advanced stage of PD, classifying correctly all cases as PD (100% sensitivity). Notably, the nominal average value of the PP for PD (0.95 (SD = 0.09)) in this cohort was higher than that of the early PD group (0.83 (SD = 0.22)), suggesting a potential for the model to assess disease severity. Lastly, the gene panel fully discriminated between PD and Alzheimer’s disease (n = 29). The findings provide evidence on the ability of a five-gene panel to diagnose early/mild PD, with a possible diagnostic value for detection of asymptomatic PD before overt expression of the disorder.
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影响因子：
11
作者：
HUGHES, AJ;DANIEL, SE;LEES, AJ
通讯作者：
LEES, AJ
影响因子：
64.5
作者：
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影响因子：
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作者：
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影响因子：
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作者：
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通讯作者：
Perl, DP
影响因子：
3.7
作者：
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通讯作者：
Muglia P