剖析和预测不同的 tau 进展模式：一种基于无监督数据驱动的 flortaucipir 正电子发射断层扫描方法。

Profiling and predicting distinct tau progression patterns: An unsupervised data-driven approach to flortaucipir positron emission tomography.

机构信息

Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA.

Northern California Institute of Research and Education, San Francisco, California, USA.

出版信息

Alzheimers Dement. 2023 Dec;19(12):5605-5619. doi: 10.1002/alz.13164. Epub 2023 Jun 8.

DOI:10.1002/alz.13164

PMID:37288753

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10704003/

Abstract

INTRODUCTION

How to detect patterns of greater tau burden and accumulation is still an open question.

METHODS

An unsupervised data-driven whole-brain pattern analysis of longitudinal tau positron emission tomography (PET) was used first to identify distinct tau accumulation profiles and then to build baseline models predictive of tau-accumulation type.

RESULTS

The data-driven analysis of longitudinal flortaucipir PET from studies done by the Alzheimer's Disease Neuroimaging Initiative, Avid Pharmaceuticals, and Harvard Aging Brain Study (N = 348 cognitively unimpaired, N = 188 mild cognitive impairment, N = 77 dementia), yielded three distinct flortaucipir-progression profiles: stable, moderate accumulator, and fast accumulator. Baseline flortaucipir levels, amyloid beta (Aβ) positivity, and clinical variables, identified moderate and fast accumulators with 81% and 95% positive predictive values, respectively. Screening for fast tau accumulation and Aβ positivity in early Alzheimer's disease, compared to Aβ positivity with variable tau progression profiles, required 46% to 77% lower sample size to achieve 80% power for 30% slowing of clinical decline.

DISCUSSION

Predicting tau progression with baseline imaging and clinical markers could allow screening of high-risk individuals most likely to benefit from a specific treatment regimen.

摘要

简介

如何检测到更大的 tau 负担和积累模式仍然是一个悬而未决的问题。

方法

首先使用无监督的数据驱动的全脑纵向 tau 正电子发射断层扫描（PET）模式分析来识别不同的 tau 积累特征，然后构建预测 tau 积累类型的基线模型。

结果

对来自阿尔茨海默病神经影像学倡议、Avid 制药公司和哈佛衰老大脑研究（N=348 名认知正常、N=188 名轻度认知障碍、N=77 名痴呆）的纵向氟脱氧酪氨酸 PET 数据进行的数据分析，得出了三种不同的氟脱氧酪氨酸进展特征：稳定型、中度积累型和快速积累型。基线氟脱氧酪氨酸水平、淀粉样蛋白β（Aβ）阳性和临床变量可分别识别中度和快速积累者，阳性预测值分别为 81%和 95%。与 Aβ 阳性且 tau 进展特征不同相比，在早期阿尔茨海默病中筛查快速 tau 积累和 Aβ 阳性，需要 46%至 77%的样本量减少，才能实现 80%的功效，即临床下降减缓 30%。