Mantovani Elisa, Martini Alice, Dinoto Alessandro, Zucchella Chiara, Ferrari Sergio, Mariotto Sara, Tinazzi Michele, Tamburin Stefano
Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy.
School of Psychology, Keele University, Newcastle, UK.
NPJ Parkinsons Dis. 2024 Nov 2;10(1):211. doi: 10.1038/s41531-024-00823-x.
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.
认知障碍(CI)在α-突触核蛋白病中很常见,即帕金森病、路易体痴呆和多系统萎缩。我们总结了关于α-突触核蛋白病中CI的神经影像学、神经生理学、生物流体和基因诊断/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改变、异常的皮质淀粉样蛋白和tau沉积、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、脑脊液/血清白蛋白商增加和血清神经丝轻链增加。预后生物标志物包括脑区萎缩、皮质节律减慢、脑脊液淀粉样蛋白生物标志物、Val66Met多态性以及载脂蛋白Eε2和ε4等位基因。一些AD/淀粉样蛋白/tau生物标志物可能诊断/预测α-突触核蛋白病中的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。