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Nonlinear biomarker interactions in conversion from mild cognitive impairment to Alzheimer's disease.
Human Brain Mapping ( IF 3.5 ) Pub Date : 2020-07-09 , DOI: 10.1002/hbm.25133
Sebastian G Popescu 1, 2 , Alex Whittington 1, 3 , Roger N Gunn 3, 4, 5 , Paul M Matthews 5, 6 , Ben Glocker 2 , David J Sharp 1, 6 , James H Cole 1, 7, 8, 9 ,
Affiliation  

Multiple biomarkers can capture different facets of Alzheimer's disease. However, statistical models of biomarkers to predict outcomes in Alzheimer's rarely model nonlinear interactions between these measures. Here, we used Gaussian Processes to address this, modelling nonlinear interactions to predict progression from mild cognitive impairment (MCI) to Alzheimer's over 3 years, using Alzheimer's Disease Neuroimaging Initiative (ADNI) data. Measures included: demographics, APOE4 genotype, CSF (amyloid‐β42, total tau, phosphorylated tau), [18F]florbetapir, hippocampal volume and brain‐age. We examined: (a) the independent value of each biomarker; and (b) whether modelling nonlinear interactions between biomarkers improved predictions. Each measured added complementary information when predicting conversion to Alzheimer's. A linear model classifying stable from progressive MCI explained over half the variance (R2 = 0.51, p < .001); the strongest independently contributing biomarker was hippocampal volume (R2 = 0.13). When comparing sensitivity of different models to progressive MCI (independent biomarker models, additive models, nonlinear interaction models), we observed a significant improvement (p < .001) for various two‐way interaction models. The best performing model included an interaction between amyloid‐β‐PET and P‐tau, while accounting for hippocampal volume (sensitivity = 0.77, AUC = 0.826). Closely related biomarkers contributed uniquely to predict conversion to Alzheimer's. Nonlinear biomarker interactions were also implicated, and results showed that although for some patients adding additional biomarkers may add little value (i.e., when hippocampal volume is high), for others (i.e., with low hippocampal volume) further invasive and expensive examination may be warranted. Our framework enables visualisation of these interactions, in individual patient biomarker ‘space', providing information for personalised or stratified healthcare or clinical trial design.

中文翻译:

从轻度认知障碍到阿尔茨海默病的非线性生物标志物相互作用。

多种生物标志物可以捕捉阿尔茨海默病的不同方面。然而,用于预测阿尔茨海默病结果的生物标志物统计模型很少模拟这些测量之间的非线性相互作用。在这里,我们使用高斯过程来解决这个问题,使用阿尔茨海默病神经影像学倡议 (ADNI) 数据对非线性相互作用进行建模,以预测从轻度认知障碍 (MCI) 到阿尔茨海默病的进展超过 3 年。测量包括:人口统计学、APOE4 基因型、CSF(淀粉样蛋白-β42、总 tau、磷酸化 tau)、[18 F]florbetapir、海马体积和脑年龄。我们检查了:(a)每个生物标志物的独立价值;(b) 对生物标志物之间的非线性相互作用进行建模是否改进了预测。在预测转化为阿尔茨海默氏症时,每项测量都添加了补充信息。从渐进 MCI 中分类稳定的线性模型解释了一半以上的方差(R 2 = 0.51,p  < .001);最强的独立贡献的生物标志物是海马体积(R 2 = 0.13)。当比较不同模型对渐进式 MCI(独立生物标志物模型、加性模型、非线性交互模型)的敏感性时,我们观察到显着改善(p < .001) 用于各种双向交互模型。表现最佳的模型包括淀粉样蛋白-β-PET 和 P-tau 之间的相互作用,同时考虑了海马体积(敏感性 = 0.77,AUC = 0.826)。密切相关的生物标志物在预测转化为阿尔茨海默病方面做出了独特的贡献。非线性生物标志物相互作用也受到牵连,结果表明,尽管对于某些患者添加额外的生物标志物可能没有什么价值(即当海马体积高时),但对于其他患者(即海马体积低),可能需要进一步的侵入性和昂贵的检查. 我们的框架能够在个体患者生物标志物“空间”中可视化这些相互作用,为个性化或分层的医疗保健或临床试验设计提供信息。
更新日期:2020-07-09
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