Transferability of Alzheimer's disease progression subtypes to an independent population cohort

• Published in: NeuroImage (Orlando, Fla.) Fla.), 2023-05, Vol.271, p.120005-120005, Article 120005
• Main Authors: Chen, Hanyi, Young, Alexandra, Oxtoby, Neil P., Barkhof, Frederik, Alexander, Daniel C., Altmann, Andre
• Format: Article
• Language: English
• Subjects: Alzheimer Disease - pathology; Alzheimer's disease; Atrophy; Atrophy - pathology; Biobanks; Biomarkers; Blood pressure; Brain - pathology; Brain research; Cerebrospinal fluid; Cholesterol; Cognitive ability; Datasets; Disease Progression; Early risk factors; Health risk assessment; Humans; Hypertension; Longitudinal studies; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical imaging; Modelling; Mutation; Neurodegenerative diseases; Neuroimaging; Neuroimaging - methods; Population; Population studies; Quality control; Risk factors; Subtypes
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2023.120005

•Three consistent atrophy subtypes were found in both ADNI and UK biobank datasets.•High consistency in individuals’ subtype and stage assignment under different models.•Data harmonization is essential to achieve successful model transfer.•The typical subtype shows the most AD-like CSF biomarker profile.•Cholesterol and blood pressure medications are associated with subtypes. 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 recover