大规模脑脊液蛋白质组分析鉴定出用于额颞叶痴呆准确诊断的生物标志物。

Large-scale CSF proteome profiling identifies biomarkers for accurate diagnosis of frontotemporal dementia.

作者信息

Hok-A-Hin Yanaika S, Vermunt Lisa, Peeters Carel F W, van der Ende Emma L, de Boer Sterre C M, Meeter Lieke H, de Houwer Julie, Seelaar Harro, van Swieten John C, Hu William T, Lleó Alberto, Alcolea Daniel, Engelborghs Sebastiaan, Sieben Anne, Chen-Plotkin Alice, Irwin David J, van der Flier Wiesje M, Pijnenburg Yolande A L, Teunissen Charlotte E, Del Campo Marta

机构信息

Neurochemistry Laboratory, Department of Laboratory Medicine, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, Amsterdam, The Netherlands.

Alzheimer Center, Department of Neurology, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, Amsterdam, The Netherlands.

出版信息

Mol Neurodegener. 2025 Aug 27;20(1):93. doi: 10.1186/s13024-025-00882-5.

DOI:10.1186/s13024-025-00882-5

PMID:40866991

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12392567/

Abstract

BACKGROUND

Diagnosis of Frontotemporal dementia (FTD) and its specific underlying neuropathologies (frontotemporal lobar degeneration; FTLD-Tau and FTLD-TDP) are challenging, and thus, fluid biomarkers are needed to improve diagnostic accuracy.

METHODS

We used proximity extension assays to analyze 665 proteins in cerebrospinal fluid (CSF) samples from a multicenter cohort, which included patients with FTD ( = 189), Alzheimer’s Disease dementia (AD; = 232), and cognitively unimpaired individuals ( = 196). In a subset, FTLD neuropathology was determined based on phenotype or genotype (FTLD-Tau = 87 and FTLD-TDP = 67). Differences in protein expression profiles were analyzed using nested linear models. Penalized generalized linear modeling was used to identify classification protein panels, which were translated to custom multiplex assays and validated in two clinical cohorts (cohort 1: = 161; cohort 2: = 162), one autopsy-confirmed cohort ( = 100), and one genetic cohort ( = 55).

RESULTS

Forty-three proteins were differentially regulated in FTD compared to controls and AD, reflecting axon development, regulation of synapse assembly, and cell-cell adhesion mediator activity pathways. Classification analysis identified a 14- and 13-CSF protein panel that discriminated FTD from controls (FTD diagnostic panel, AUC: 0.96) or AD (FTD differential diagnostic panel, AUC: 0.91). Custom multiplex panels confirmed the strong discriminative performancen between FTD and controls (AUCs > 0.96) and between FTD and AD (AUCs > 0.88) across three validation cohorts, including one with autopsy confirmation (AUCs > 0.90). Validation in genetic FTD (including ,, and mutation carriers) revealed high accuracy of the FTD diagnostic panel in identifying both the presymptomatic (AUCs > 0.95) and symptomatic (AUC: 1) stages. Six proteins were differentially regulated between FTLD-TDP and FTLD-Tau. However, a reproducible classification model could not be generated (AUC: 0.80).

CONCLUSIONS

Overall, this study introduces novel FTD-specific biomarker panels with potential use in diagnostic settings.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1186/s13024-025-00882-5.

摘要

背景

额颞叶痴呆（FTD）及其特定的潜在神经病理学（额颞叶变性；FTLD-Tau和FTLD-TDP）的诊断具有挑战性，因此，需要体液生物标志物来提高诊断准确性。

方法

我们使用邻近延伸分析来分析来自多中心队列的脑脊液（CSF）样本中的665种蛋白质，该队列包括FTD患者（n = 189）、阿尔茨海默病痴呆（AD；n = 232）和认知未受损个体（n = 196）。在一个亚组中，根据表型或基因型确定FTLD神经病理学（FTLD-Tau = 87和FTLD-TDP = 67）。使用嵌套线性模型分析蛋白质表达谱的差异。采用惩罚广义线性模型来识别分类蛋白质组，将其转化为定制的多重分析，并在两个临床队列（队列1：n = 161；队列2：n = 162）、一个尸检确诊队列（n = 100）和一个遗传队列（n = 55）中进行验证。

结果

与对照组和AD相比，FTD中有43种蛋白质的调节存在差异，反映了轴突发育、突触组装调节和细胞间粘附介质活性途径。分类分析确定了一个由14种和13种脑脊液蛋白质组成的小组，可将FTD与对照组（FTD诊断小组，AUC：0.96）或AD（FTD鉴别诊断小组，AUC：0.91）区分开来。定制的多重分析小组在三个验证队列中证实了FTD与对照组（AUCs > 0.96）以及FTD与AD之间（AUCs > 0.88）具有很强的鉴别性能，包括一个有尸检确认的队列（AUCs > 0.90）。在遗传性FTD（包括、和突变携带者）中的验证显示，FTD诊断小组在识别症状前（AUCs > 0.95）和症状期（AUC：1）具有很高的准确性。FTLD-TDP和FTLD-Tau之间有6种蛋白质的调节存在差异。然而，无法生成可重复的分类模型（AUC：0.80）。