元素指纹：重新评估帕金森氏病的脑脊液生物标志物。,Neurobiology of Disease

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元素指纹：重新评估帕金森氏病的脑脊液生物标志物。
Neurobiology of Disease ( IF 5.1 ) Pub Date : 2019-11-13 , DOI: 10.1016/j.nbd.2019.104677
Fabian Maass ₁ , Bernhard Michalke ₂ , Desiree Willkommen ₂ , Andreas Leha ₃ , Claudia Schulte ₄ , Lars Tönges ₅ , Brit Mollenhauer ₆ , Claudia Trenkwalder ₇ , Daniel Rückamp ₈ , Matthias Börger ₁ , Inga Zerr ₉ , Mathias Bähr ₁₀ , Paul Lingor ₁₁

Affiliation

Department of Neurology, University Medical Center Goettingen, Germany.
Research Unit Analytical BioGeoChemistry, German Research Center for Environmental Health, Helmholtz Zentrum Munich, Neuherberg, Germany.
Department of Medical Statistics, University Medical Center, Goettingen, Germany.
DZNE, German Center for Neurodegenerative Diseases, University of Tuebingen, Germany; Center of Neurology, Department of Neurodegeneration and Hertie-Institute for Clinical Brain Research, University of Tuebingen, Germany.
Department of Neurology, Ruhr-University Bochum, St. Josef-Hospital, Bochum, Germany.
Department of Neurology, University Medical Center Goettingen, Germany; Paracelsus-Elena-Klinik, Kassel, Germany.
Paracelsus-Elena-Klinik, Kassel, Germany; Dept. Neurosurgery, University Medical Center, Goettingen, Germany.
Federal Institute for Geosciences and Natural Resources, Hannover, Germany.
Department of Neurology, University Medical Center Goettingen, Germany; DZNE, German Center for Neurodegenerative Diseases Goettingen, Germany.
Department of Neurology, University Medical Center Goettingen, Germany; Cluster of Excellence Nanoscale Microscopy and Molecular Physiology of the Brain (CNMPB), Goettingen, Germany.
Department of Neurology, University Medical Center Goettingen, Germany; DZNE, German Center for Neurodegenerative Diseases Goettingen, Germany; Cluster of Excellence Nanoscale Microscopy and Molecular Physiology of the Brain (CNMPB), Goettingen, Germany; Center for Biostructural Imaging of Neurodegeneration (BIN), University Medical Center Goettingen, Germany; Department of Neurology, Klinikum rechts der Isar, School of Medicine, Technical University Munich, 81675 Munich, Germany.

这项研究的目的是基于脑脊髓液（CSF）中的生物元素丰度，验证一种用于分类帕金森氏病（PD）和年龄匹配的对照（AMC）的预测性生物标志物机器学习模型。在这项多中心试验中，参与者来自四个不同的中心。根据标准化协议收集脑脊液。为了确定生物元素，对CSF样品进行了电感耦合等离子体质谱分析。基于六个不同生物元素的水平，将经过先前发现队列训练的预定义支持向量机（SVM）模型应用于区分。包括82名PD患者，68名年龄匹配的对照和7名其他正常压力脑积水（NPH）患者，以验证预定义的SVM模型。六种区分元素（砷，铁，镁，镍，硒，Sr）被定量。基于它们的级别，SVM已成功应用于新的本地队列（AUROC 0.76，灵敏度0.80，特异性0.83），而没有考虑任何其他功能。相同的模型可能没有将PD和AMC / NPH与三个外部队列区分开，这可能是由于中心效应所致。但是，现在可以使用特定于中心的发现队列和交叉验证的方法（具有AUROC 0.78和0.88）对具有完整元素数据集的队列进行区分。合并的PD CSF铁水平与疾病持续时间有明显的相关性（p = .0001）。总之，通过质谱法获得并整合到预测模型中的生物元素CSF模式具有促进PD和AMC分化的潜力。中心偏倚会干扰外部同类群组的应用。

"点击查看英文标题和摘要"

Elemental fingerprint: Reassessment of a cerebrospinal fluid biomarker for Parkinson's disease.

The aim of the study was to validate a predictive biomarker machine learning model for the classification of Parkinson's disease (PD) and age-matched controls (AMC), based on bioelement abundance in the cerebrospinal fluid (CSF). For this multicentric trial, participants were enrolled from four different centers. CSF was collected according to standardized protocols. For bioelement determination, CSF samples were subjected to inductively coupled plasma mass spectrometry. A predefined Support Vector Machine (SVM) model, trained on a previous discovery cohort was applied for differentiation, based on the levels of six different bioelements. 82 PD patients, 68 age-matched controls and 7 additional Normal Pressure Hydrocephalus (NPH) patients were included to validate a predefined SVM model. Six differentiating elements (As, Fe, Mg, Ni, Se, Sr) were quantified. Based on their levels, SVM was successfully applied to a new local cohort (AUROC 0.76, Sensitivity 0.80, Specificity 0.83), without taking any additional features into account. The same model did not discriminate PD and AMCs / NPH from three external cohorts, likely due to center effects. However, discrimination was possible in cohorts with a full elemental data set, now using center-specific discovery cohorts and a cross validated approach (AUROC 0.78 and 0.88, respectively). Pooled PD CSF iron levels showed a clear correlation with disease duration (p = .0001). In summary, bioelemental CSF patterns, obtained by mass spectrometry and integrated into a predictive model yield the potential to facilitate the differentiation of PD and AMC. Center-specific biases interfere with application in external cohorts. This must be carefully addressed using center-defined, local reference values and models.

更新日期：2019-11-13

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