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• W2897431372 abstract "Draft guidelines from the NIA-AA and Jack et al. (2016, 2017) describe an “unbiased descriptive classification scheme” for Alzheimer's disease (AD) based on established AD biomarkers: amyloid (A), phosphorylated Tau (T) and total Tau (N for neurodegeneration). The simplest instantiation of this framework considers a binary threshold on each of these three biomarkers, leading to 8 classes. While these ATN profiles have been explored in cross-sectional data, here we examine their utility in estimating clinically relevant longitudinal outcomes. Using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, we examine 948 individuals (62 cognitively normal (CN), 618 mild cognitively impaired (MCI) and 228 AD) for whom CSF measurements of A, T and N were available at baseline, measured using the Elecsys platform. Cutoffs for each biomarker were 962.1 ml, 24.9 ml and, 263.1ml for A,T and N respectively, determined by mixture modelling. Cox regression and mixed linear models were used to evaluate longitudinal outcomes: conversion from CN/MCI to AD, cognitive decline and hippocampal volume. Comparison between regression models was evaluated using likelihood ratio tests. Breakdown of the ATN groupings is shown in Table 1. Cross sectional analysis at baseline shows that the association between ATN stage and APOE4 carrier status, cognitive scores and hippocampal volume at baseline is not significantly better than looking at A/T status alone (Table 2, p>0.05). Differences in conversion rates of MCI and CN individuals to AD across the 8 ATN stages were inline with expected staging progression (Fig 1, A- groups rarely convert to AD, A+T+N+ converted most rapidly). Cox regression accounting for age, gender and APOE4 showed the ATN model performed significantly better than A/T or A/N staging alone (p= 1.13′10−11 and 4.12′10−11). However, for cognitive decline and brain atrophy, we found no significant improvement in modelling using A/T/N staging compared to A/T alone (Table 3, p>0.05), with genetic risk factors having a more significant impact. Our results help to characterize the utility of ATN staging with respect to longitudinal outcomes. The inclusion of N beyond A and T is useful in estimating rates of conversion to AD, with limited improvements shown in other longitudinal analyses." @default.
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• W2897431372 date "2018-07-01" @default.
• W2897431372 modified "2023-10-16" @default.
• W2897431372 title "P1‐300: A LONGITUDINAL ANALYSIS OF THE ATN STAGING CRITERIA" @default.
• W2897431372 doi "https://doi.org/10.1016/j.jalz.2018.06.306" @default.
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