Tau extent outperforms tau load as a predictor of neurodegeneration in Alzheimer’s disease

神经退行性变 陶氏病 神经科学 神经学 萎缩 医学 病理 进行性核上麻痹 相关性 疾病 阿尔茨海默病 失智症 生物标志物 神经影像学 海马结构 内科学 标准摄取值 痴呆 生物 τ蛋白 结合势 脑脊液 大脑大小 预测值 心理学
作者
Arthur C. Macedo,Lydia Trudel,Seyyed Ali Hosseini,Joseph Therriault,Étienne Aumont,Gleb Bezgin,Nesrine Rahmouni,Jenna Stevenson,Cécile Tissot,Marcel S. Woo,Stijn Servaes,Stuart Mitchell,Jaime Fernandez‐Arias,Brandon Hall,Tevy Chan,Marina Pereira Gonçalves,Pamela C. L. Ferreira,João Pedro Ferrari-Souza,Bruna Bellaver,Firoza Z. Lussier
出处
期刊:Molecular Neurodegeneration [BioMed Central]
标识
DOI:10.1186/s13024-026-00945-1
摘要

BACKGROUND: In Alzheimer's disease (AD), tau pathology is more strongly linked to neurodegeneration than amyloid-β and better predicts brain atrophy. The spatial extent of tauopathy (SEOT) has shown promise as an earlier and more sensitive marker of AD severity than tau load, but how these complementary dimensions relate to neurodegeneration remains unclear. Here, we compared the in vivo associations of tau-PET extent versus load with cross-sectional and longitudinal neurodegeneration. METHODS: F]MK-6240 tau-PET. Tau load was quantified as regional standardized uptake value ratio (SUVR), and SEOT as the proportion of abnormal voxels, within a temporal meta-region of interest (ROI) and a full-cortex ROI. Neurodegeneration markers included cortical thickness, hippocampal volume (HCV), medial temporal atrophy (MTA) visual ratings, plasma neurofilament light (NfL), and CSF total tau (t-tau). Cross-sectional associations were evaluated using multiple linear regression or covariate-adjusted Spearman correlations. We also compared local correlations of tau load and extent with cortical thickness across all cortical regions. Longitudinal predictive value for neurodegeneration was tested using linear mixed-effects models. RESULTS: Cross-sectionally, all tau-PET metrics were significantly associated with neurodegeneration across imaging and fluid biomarkers. Full-cortex SEOT provided the best model fit for cortical thinning. SEOT outperformed tau load for associations with HCV and for predicting MTA, whereas SEOT and SUVR showed comparable associations with plasma NfL and CSF t-tau. Tau extent was equal or superior to tau load in its correlation with local cortical thickness across all cortical regions. Longitudinally, baseline full-cortex SEOT best predicted future cortical thinning, while temporal SEOT best predicted future hippocampal atrophy. CONCLUSIONS: Across cross-sectional and longitudinal analyses, tau extent provided superior predictive value for imaging-based neurodegeneration compared with tau load. By enabling a spatially unbiased, whole-brain assessment of tau burden that accommodates heterogeneous topographies, SEOT represents a promising complementary tau-PET metric for staging and tracking disease progression in AD.
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