Plasma fibronectin is a prognostic biomarker of disability in Parkinson’s disease: a prospective, multicenter cohort study

IF 6.7 1区 医学 Q1 NEUROSCIENCES NPJ Parkinson's Disease Pub Date : 2025-01-02 DOI:10.1038/s41531-024-00865-1
Shuzhen Zhu, Hualin Li, Zifeng Huang, Yiheng Zeng, Jianmin Huang, Guixia Li, Shujuan Yang, Hang Zhou, Zihan Chang, Zhenchao Xie, Rongfang Que, Xiaobo Wei, Minzi Li, Yanran Liang, Wenbiao Xian, Mengyan Li, Ying Pan, Fanheng Huang, Lin Shi, Chengwu Yang, Chao Deng, Lucia Batzu, Karolina Poplawska-Domaszewicz, Shuhan Chen, Ling-Ling Chan, K Ray Chaudhuri, Eng-King Tan, Qing Wang
{"title":"Plasma fibronectin is a prognostic biomarker of disability in Parkinson’s disease: a prospective, multicenter cohort study","authors":"Shuzhen Zhu, Hualin Li, Zifeng Huang, Yiheng Zeng, Jianmin Huang, Guixia Li, Shujuan Yang, Hang Zhou, Zihan Chang, Zhenchao Xie, Rongfang Que, Xiaobo Wei, Minzi Li, Yanran Liang, Wenbiao Xian, Mengyan Li, Ying Pan, Fanheng Huang, Lin Shi, Chengwu Yang, Chao Deng, Lucia Batzu, Karolina Poplawska-Domaszewicz, Shuhan Chen, Ling-Ling Chan, K Ray Chaudhuri, Eng-King Tan, Qing Wang","doi":"10.1038/s41531-024-00865-1","DOIUrl":null,"url":null,"abstract":"<p>In a prospective longitudinal study with 218 Parkinson’s disease (PD) patients in the discovery cohort and 84 in the validation cohort, we aimed to identify novel blood biomarkers predicting disability milestones in PD. Through Least Absolute Shrinkage and Selection Operator-Cox (Lasso-Cox) regression, developed nomogram predictive model and Linear mixed-effects models, we identified low level of plasma fibronectin (pFN) as one of the best-performing risk markers in predicting disability milestones. A low level of pFN was associated with a short milestone-free survival period in PD. Longitudinal analysis showed an annual decline in the rate of pFN was significantly associated with the annual elevation rate in the Hoehn-Yahr stage. Moreover, pFN level was negatively correlated with phosphorylated α-synuclein, and a low level of pFN was associated with BBB disruption in the striatum on neuroimaging, providing evidence for pFN’s role in PD progression. We finally identified pFN as a novel blood biomarker that predicted first-milestone disability in PD.</p>","PeriodicalId":19706,"journal":{"name":"NPJ Parkinson's Disease","volume":"13 1","pages":""},"PeriodicalIF":6.7000,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"NPJ Parkinson's Disease","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1038/s41531-024-00865-1","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

In a prospective longitudinal study with 218 Parkinson’s disease (PD) patients in the discovery cohort and 84 in the validation cohort, we aimed to identify novel blood biomarkers predicting disability milestones in PD. Through Least Absolute Shrinkage and Selection Operator-Cox (Lasso-Cox) regression, developed nomogram predictive model and Linear mixed-effects models, we identified low level of plasma fibronectin (pFN) as one of the best-performing risk markers in predicting disability milestones. A low level of pFN was associated with a short milestone-free survival period in PD. Longitudinal analysis showed an annual decline in the rate of pFN was significantly associated with the annual elevation rate in the Hoehn-Yahr stage. Moreover, pFN level was negatively correlated with phosphorylated α-synuclein, and a low level of pFN was associated with BBB disruption in the striatum on neuroimaging, providing evidence for pFN’s role in PD progression. We finally identified pFN as a novel blood biomarker that predicted first-milestone disability in PD.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
血浆纤维连接蛋白是帕金森病致残的预后生物标志物:一项前瞻性、多中心队列研究
在一项前瞻性纵向研究中,218名帕金森病(PD)患者在发现队列和84名验证队列中,我们旨在确定预测PD残疾里程碑的新的血液生物标志物。通过最小绝对收缩和选择算子-考克斯(Lasso-Cox)回归,建立了nomogram预测模型和线性混合效应模型,我们发现血浆纤维连接蛋白(pFN)水平低是预测残疾里程碑的最佳风险指标之一。低水平的pFN与PD患者短的无里程碑生存期相关。纵向分析显示,在Hoehn-Yahr期,pFN的年递减率与年递减率显著相关。此外,pFN水平与α-突触核蛋白磷酸化呈负相关,在神经影像学上,低水平的pFN与纹状体血脑屏障破坏有关,这为pFN在PD进展中的作用提供了证据。我们最终确定pFN是一种新的血液生物标志物,可预测PD患者的第一里程碑残疾。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
NPJ Parkinson's Disease
NPJ Parkinson's Disease Medicine-Neurology (clinical)
CiteScore
9.80
自引率
5.70%
发文量
156
审稿时长
11 weeks
期刊介绍: npj Parkinson's Disease is a comprehensive open access journal that covers a wide range of research areas related to Parkinson's disease. It publishes original studies in basic science, translational research, and clinical investigations. The journal is dedicated to advancing our understanding of Parkinson's disease by exploring various aspects such as anatomy, etiology, genetics, cellular and molecular physiology, neurophysiology, epidemiology, and therapeutic development. By providing free and immediate access to the scientific and Parkinson's disease community, npj Parkinson's Disease promotes collaboration and knowledge sharing among researchers and healthcare professionals.
期刊最新文献
VisionMD: an open-source tool for video-based analysis of motor function in movement disorders Author Correction: MRgFUS subthalamotomy in Parkinson's disease: an approach aimed at minimizing Lesion Volume. Clinically probable RBD is an early predictor of malignant non-motor Parkinson’s disease phenotypes Morphological and functional decline of the SNc in a model of progressive parkinsonism Multiomics approach identifies dysregulated lipidomic and proteomic networks in Parkinson’s disease patients mutated in TMEM175
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1