Shand Cameron, Markiewicz Pawel J, Cash David M, Alexander Daniel C, Donohue Michael C, Barkhof Frederik, Oxtoby Neil P
Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.
Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK.
medRxiv. 2023 Feb 10:2023.02.07.23285572. doi: 10.1101/2023.02.07.23285572.
Undetected biological heterogeneity adversely impacts trials in Alzheimer's disease because rate of cognitive decline - and perhaps response to treatment - differs in subgroups. Recent results show that data-driven approaches can unravel the heterogeneity of Alzheimer's disease progression. The resulting stratification is yet to be leveraged in clinical trials.
Investigate whether image-based data-driven disease progression modelling could identify baseline biological heterogeneity in a clinical trial, and whether these subgroups have prognostic or predictive value.
Screening data from the Anti-Amyloid Treatment in Asymptomatic Alzheimer Disease (A4) Study collected between April 2014 and December 2017, and longitudinal data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) observational study downloaded in February 2022 were used.
The A4 Study is an interventional trial involving 67 sites in the US, Canada, Australia, and Japan. ADNI is a multi-center observational study in North America.
Cognitively unimpaired amyloid-positive participants with a 3-Tesla T1-weighted MRI scan. Amyloid positivity was determined using florbetapir PET imaging (in A4) and CSF Aβ(1-42) (in ADNI).
Regional volumes estimated from MRI scans were used as input to the Subtype and Stage Inference (SuStaIn) algorithm. Outcomes included cognitive test scores and SUVr values from florbetapir and flortaucipir PET.
We included 1,240 Aβ+ participants (and 407 Aβ- controls) from the A4 Study, and 731 A4-eligible ADNI participants. SuStaIn identified three neurodegeneration subtypes - - comprising 523 (42%) individuals. The remainder are designated subtype zero (insufficient atrophy). Baseline PACC scores (A4 primary outcome) were significantly worse in the subtype (median = -1.27, IQR=[-3.34,0.83]) relative to both subtype zero (median=-0.013, IQR=[-1.85,1.67], P<.0001) and the subtype (median=0.03, IQR=[-1.78,1.61], P=.0006). In ADNI, over a four-year period (comparable to A4), greater cognitive decline in the mPACC was observed in both the (-0.23/yr; 95% CI, [-0.41,-0.05]; P=.01) and (-0.24/yr; [-0.42,-0.06]; P=.009) subtypes, as well as the CDR-SB (: +0.09/yr, [0.06,0.12], P<.0001; and : +0.07/yr, [0.04,0.10], P<.0001).
In a large secondary prevention trial, our image-based model detected neurodegenerative heterogeneity predictive of cognitive heterogeneity. We argue that such a model is a valuable tool to be considered in future trial design to control for previously undetected variance.
未被检测到的生物学异质性对阿尔茨海默病的试验产生不利影响，因为认知衰退率（可能还有对治疗的反应）在亚组中存在差异。最近的结果表明，数据驱动方法可以揭示阿尔茨海默病进展的异质性。由此产生的分层尚未在临床试验中得到应用。
研究基于图像的数据驱动疾病进展模型是否能够在一项临床试验中识别基线生物学异质性，以及这些亚组是否具有预后或预测价值。
使用了2014年4月至2017年12月期间收集的无症状阿尔茨海默病抗淀粉样蛋白治疗（A4）研究的筛查数据，以及2022年2月下载的阿尔茨海默病神经影像倡议（ADNI）观察性研究的纵向数据。
A4研究是一项涉及美国、加拿大、澳大利亚和日本67个地点的干预性试验。ADNI是一项在北美的多中心观察性研究。
认知未受损的淀粉样蛋白阳性参与者，有3特斯拉T1加权磁共振成像扫描。淀粉样蛋白阳性通过氟代硼吡咯PET成像（在A4研究中）和脑脊液Aβ(1 - 42)（在ADNI中）来确定。
从磁共振成像扫描估计的区域体积被用作亚型和阶段推断（SuStaIn）算法的输入。结局包括认知测试分数以及氟代硼吡咯和氟替卡匹PET的SUVr值。
我们纳入了A4研究中的1240名Aβ+参与者（以及407名Aβ-对照），以及731名符合A4标准的ADNI参与者。SuStaIn识别出三种神经退行性变亚型——包含523名（4·2%）个体。其余被指定为零亚型（萎缩不足）。相对于零亚型（中位数 = -0·013，IQR = [-1·85,1·67]，P <.0001）和 亚型（中位数 = 0·03，IQR = [-1·78,1·61]，P =.0006）， 亚型的基线PACC分数（A4主要结局）显著更差（中位数 = -1·27，IQR = [-3·34,0·83]）。在ADNI中，在四年期间（与A4相当）， 亚型（-0·23/年；95% CI，[-0·41,-0·05]；P =.01）和 亚型（-0·24/年；[-0·42,-0·06]；P =.009）以及CDR - SB（ 亚型：+0·09/年，[0·06,0·12]，P <.0001； 亚型：+0·07/年，[0·04,0·10]，P <.0001）均观察到更大的mPACC认知衰退。
在一项大型二级预防试验中，我们基于图像的模型检测到了可预测认知异质性的神经退行性异质性。我们认为这样的模型是未来试验设计中可考虑用于控制先前未检测到变异的有价值工具。
