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帕金森病的生物标志物:它们有多好?

Biomarkers for Parkinson's Disease: How Good Are They?

机构信息

Center for Clinical Research on Neurological Diseases, the First Affiliated Hospital, Dalian Medical University, Dalian, 116021, China.

Liaoning Provincial Key Laboratory for Research on the Pathogenic Mechanisms of Neurological Diseases, the First Affiliated Hospital, Dalian Medical University, Dalian, 116021, China.

出版信息

Neurosci Bull. 2020 Feb;36(2):183-194. doi: 10.1007/s12264-019-00433-1. Epub 2019 Oct 23.

Abstract

Parkinson's disease (PD) is a complex neurodegenerative disorder with no cure in sight. Clinical challenges of the disease include the inability to make a definitive diagnosis at the early stages and difficulties in predicting the disease progression. The unmet demand to identify reliable biomarkers for early diagnosis and management of the disease course of PD has attracted a lot of attention. However, only a few reported candidate biomarkers have been tried in clinical practice at the present time. Studies on PD biomarkers have often overemphasized the discovery of novel identity, whereas efforts to further evaluate such candidates are rare. Therefore, we update the new development of biomarker discovery in PD and discuss the standard process in the evaluation and assessment of the diagnostic or prognostic value of the identified potential PD biomarkers in this review article. Recent developments in combined biomarkers and the current status of clinical trials of biomarkers as outcome measures are also discussed. We believe that the combination of different biomarkers might enhance the specificity and sensitivity over a single measure that might not be sufficient for such a multiplex disease.

摘要

帕金森病(PD)是一种复杂的神经退行性疾病,目前尚无治愈方法。该疾病的临床挑战包括在早期阶段无法做出明确诊断以及难以预测疾病进展。为了确定可靠的生物标志物以用于 PD 的早期诊断和管理疾病进程,人们的需求尚未得到满足,这引起了广泛关注。然而,目前只有少数已报道的候选生物标志物在临床实践中进行了尝试。PD 生物标志物的研究通常过于强调新标识的发现,而很少有努力进一步评估这些候选物。因此,我们在这篇综述文章中更新了 PD 中生物标志物发现的新进展,并讨论了对所识别的潜在 PD 生物标志物的诊断或预后价值进行评估和评估的标准过程。本文还讨论了组合生物标志物的最新进展以及生物标志物作为临床试验结果衡量指标的现状。我们相信,不同生物标志物的组合可能会提高单一指标的特异性和敏感性,因为单一指标可能不足以用于这种多因素疾病。

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