Centre for Medical Image Computing Department of Medical Physics and Biomedical Engineering and Department of Computer Science, University College London, UK.
Department of Neuroimaging Institute of Psychiatry Psychology and Neuroscience, King's College London, UK.
Neuroimage. 2023 May 1;271:120005. doi: 10.1016/j.neuroimage.2023.120005. Epub 2023 Mar 11.
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.
过去，已经开发出使用脑成像数据对患者进行亚型或生物型分类的方法。然而，尚不清楚这些经过训练的机器学习模型是否以及如何能够成功地应用于人群队列，以研究构成这些亚型的遗传和生活方式因素。这项使用亚型和阶段推断（SuStaIn）算法的工作，检验了数据驱动的阿尔茨海默病（AD）进展模型的泛化能力。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（UK Biobank）数据集构建的 AD 风险人群中训练的 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构建了 SuStaIn 模型，然后使用这些模型对另一个协调数据集的对象进行分类和分期。第一个关键发现是，在两个数据集都发现了三个一致的萎缩亚型，这与 AD 中先前确定的亚型进展模式相匹配：“典型”、“皮质”和“皮质下”。接下来，基于不同模型，个体亚型和分期的高度一致性进一步支持了亚型的一致性：在 ADNI 和 UK Biobank 数据集均具有可靠亚型分配的受试者中，超过 92%的受试者根据不同模型分配到了相同的亚型。AD 萎缩进展亚型在捕获不同疾病发展阶段的队列之间的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，典型亚型与更具 AD 样的脑脊液生物标志物值相关；（3）与皮质下亚型相比，皮质亚型的受试者更有可能与胆固醇和高血压药物的处方相关。总之，我们提出了跨队列一致的 AD 萎缩亚型恢复，展示了即使在捕获明显不同疾病阶段的队列中，相同的亚型是如何出现的。我们的研究为未来对广泛的早期风险因素进行萎缩亚型的详细研究提供了机会，这将有可能深入了解疾病的发病机制以及生活方式和行为对 AD 的作用。
