Transferability of Alzheimer's disease progression subtypes to an independent population cohort.
Transferability of Alzheimer's disease progression subtypes to an independent population cohort.
复制标题
阿尔茨海默病进展亚型向独立人群队列的可转移性。
DOI：
10.1016/j.neuroimage.2023.120005
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发表时间：
2023
期刊：
影响因子：
5.7
通讯作者：
Chen H
中科院分区：
文献类型：
--
作者：
Chen H
在过去，已经开发了使用脑成像数据对患者进行分型或生物分型的方法。然而，目前尚不清楚这些经过训练的机器学习模型是否以及如何成功应用于人群队列，以研究支持这些亚型的遗传和生活方式因素。这项工作，使用子类型和阶段推断（SuStaIn）算法，检查数据驱动的阿尔茨海默病（AD）进展模型的普适性。我们首先比较了分别在阿尔茨海默病神经影像学倡议（ADNI）数据和从英国生物银行数据集构建的AD风险人群上训练的SuStaIn模型。我们进一步应用数据协调技术来消除队列效应。接下来，我们在协调数据集上建立SuStaIn模型，然后将其用于对另一个协调数据集中的受试者进行分型和分期。第一个关键发现是，在两个数据集中发现了三种一致的萎缩亚型，它们与之前确定的AD亚型进展模式相匹配：“典型”，“皮质”和“皮质下”。其次，基于不同模型的个体亚型和分期分配的高度一致性进一步支持了亚型一致性：在ADNI和UK Biobank数据集中具有可靠亚型分配的超过92%的受试者在基于不同数据集构建的模型下被分配到相同的亚型。AD萎缩进展亚型在捕获疾病发展的不同阶段的队列中的成功可转移性使得能够进一步研究AD萎缩亚型与风险因素之间的关联。我们的研究表明：（1）典型亚型的平均年龄最高，皮质下亚型的平均年龄最低;（2）与其他两种亚型相比，典型亚型与统计学上更多的AD样脑脊液生物标志物值相关;和（3）与皮质下亚型相比，皮质亚型受试者更可能与胆固醇和高血压药物的处方有关。总之，我们提出了AD萎缩亚型的跨队列一致恢复，显示了即使在捕获实质上不同的疾病阶段的队列中，相同的亚型如何出现。我们的研究为未来详细研究具有广泛早期风险因素的萎缩亚型提供了机会，这可能会更好地了解疾病病因以及生活方式和行为对AD的作用。
In the past, methods to subtype or biotype patients using brain imaging data have been developed. However, it is unclear whether and how these trained machine learning models can be successfully applied to population cohorts to study the genetic and lifestyle factors underpinning these subtypes. This work, using the Subtype and Stage Inference (SuStaIn) algorithm, examines the generalisability of data-driven Alzheimer's disease (AD) progression models.We first compared SuStaIn models trained separately on Alzheimer's disease neuroimaging initiative (ADNI) data and an AD-at-risk population constructed from the UK Biobank dataset. We further applied data harmonization techniques to remove cohort effects. Next, we built SuStaIn models on the harmonized datasets, which were then used to subtype and stage subjects in the other harmonized dataset.The first key finding is that three consistent atrophy subtypes were found in both datasets, which match the previously identified subtype progression patterns in AD: ‘typical’, ‘cortical’ and ‘subcortical’. Next, the subtype agreement was further supported by high consistency in individuals’ subtypes and stage assignment based on the different models: more than 92% of the subjects, with reliable subtype assignment in both ADNI and UK Biobank dataset, were assigned to an identical subtype under the model built on the different datasets. The successful transferability of AD atrophy progression subtypes across cohorts capturing different phases of disease development enabled further investigations of associations between AD atrophy subtypes and risk factors. Our study showed that (1) the average age is highest in the typical subtype and lowest in the subcortical subtype; (2) the typical subtype is associated with statistically more-AD-like cerebrospinal fluid biomarkers values in comparison to the other two subtypes; and (3) in comparison to the subcortical subtype, the cortical subtype subjects are more likely to associate with prescription of cholesterol and high blood pressure medications.In summary, we presented cross-cohort consistent recovery of AD atrophy subtypes, showing how the same subtypes arise even in cohorts capturing substantially different disease phases. Our study opened opportunities for future detailed investigations of atrophy subtypes with a broad range of early risk factors, which will potentially lead to a better understanding of the disease aetiology and the role of lifestyle and behaviour on AD.