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Biomarkers for cognitive impairment in alpha-synucleinopathies: an overview of systematic reviews and meta-analyses
npj Parkinson's Disease ( IF 6.7 ) Pub Date : 2024-11-02 , DOI: 10.1038/s41531-024-00823-x
Elisa Mantovani, Alice Martini, Alessandro Dinoto, Chiara Zucchella, Sergio Ferrari, Sara Mariotto, Michele Tinazzi, Stefano Tamburin

Cognitive impairment (CI) is common in α-synucleinopathies, i.e., Parkinson’s disease, Lewy bodies dementia, and multiple system atrophy. We summarize data from systematic reviews/meta-analyses on neuroimaging, neurophysiology, biofluid and genetic diagnostic/prognostic biomarkers of CI in α-synucleinopathies. Diagnostic biomarkers include atrophy/functional neuroimaging brain changes, abnormal cortical amyloid and tau deposition, and cerebrospinal fluid (CSF) Alzheimer’s disease (AD) biomarkers, cortical rhythm slowing, reduced cortical cholinergic and glutamatergic and increased cortical GABAergic activity, delayed P300 latency, increased plasma homocysteine and cystatin C and decreased vitamin B12 and folate, increased CSF/serum albumin quotient, and serum neurofilament light chain. Prognostic biomarkers include brain regional atrophy, cortical rhythm slowing, CSF amyloid biomarkers, Val66Met polymorphism, and apolipoprotein-E ε2 and ε4 alleles. Some AD/amyloid/tau biomarkers may diagnose/predict CI in α-synucleinopathies, but single, validated diagnostic/prognostic biomarkers lack. Future studies should include large consortia, biobanks, multi-omics approach, artificial intelligence, and machine learning to better reflect the complexity of CI in α-synucleinopathies.



中文翻译:


α-突触核蛋白病认知障碍的生物标志物:系统评价和荟萃分析概述



认知障碍 (CI) 常见于α突触核蛋白病,即帕金森病、路易体痴呆和多系统萎缩。我们总结了来自神经影像学、神经生理学、生物流体和 α-突触核蛋白病 CI 的遗传诊断/预后生物标志物的系统评价/荟萃分析数据。诊断生物标志物包括萎缩/功能性神经影像学脑变化、异常皮质淀粉样蛋白和 tau 蛋白沉积以及脑脊液 (CSF) 阿尔茨海默病 (AD) 生物标志物、皮质节律减慢、皮质胆碱能和谷氨酸能降低以及皮质 GABA 能活性增加、P300 潜伏期延迟、血浆同型半胱氨酸和胱抑素 C 增加以及维生素 B12 和叶酸降低、CSF/血清白蛋白商增加和血清神经丝轻链。预后生物标志物包括脑区域萎缩、皮质节律减慢、CSF 淀粉样蛋白生物标志物、Val66Met 多态性以及载脂蛋白 E ε2 和 ε4 等位基因。一些 AD/淀粉样蛋白/tau 蛋白生物标志物可以诊断/预测 α-突触核蛋白病的 CI,但缺乏单一的、经过验证的诊断/预后生物标志物。未来的研究应包括大型联盟、生物样本库、多组学方法、人工智能和机器学习,以更好地反映 α-突触核蛋白病中 CI 的复杂性。

更新日期:2024-11-03
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