TY - JOUR
T1 - Transferability of Alzheimer's disease progression subtypes to an independent population cohort
AU - Chen, Hanyi
AU - Young, Alexandra
AU - Oxtoby, Neil P.
AU - ADNI investigators
AU - Barkhof, Frederik
AU - Alexander, Daniel C.
AU - Altmann, Andre
N1 - Funding Information:
This study was supported by the Early Detection of Alzheimer's Disease Subtypes (E-DADS) project, an EU Joint Programme - Neurodegenerative Disease Research (JPND) project (see www.jpnd.eu). The project is supported under the aegis of JPND through the following funding organizations: United Kingdom, Medical Research Council (MR/T046422/1); Netherlands, ZonMW (733051106); France, Agence Nationale de la Recherche (ANR-19-JPW2–000); Italy, Italian Ministry of Health (MoH); Australia, National Health & Medical Research Council (1191535); Hungary, National Research, Development and Innovation Office (2019–2.1.7-ERA-NET-2020–00008). This study was also supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre and EPSRC grant EP/M020533/1. A.L.Y is supported by a Skills Development Fellowship from the Medical Research Council (MR/T027800/1). F.B. is a National Institute of Health Research (NIHR) Professor. N.P.O. is a UKRI Future Leaders Fellow (MR/S03546X/1). A.A. holds an MRC eMedLab Medical Bioinformatics Career Development Fellowship. This work was partly supported by the Medical Research Council (MR/L016311/1].
Funding Information:
This study was supported by the Early Detection of Alzheimer's Disease Subtypes (E-DADS) project, an EU Joint Programme - Neurodegenerative Disease Research (JPND) project (see www.jpnd.eu ). The project is supported under the aegis of JPND through the following funding organizations: United Kingdom, Medical Research Council (MR/T046422/1); Netherlands, ZonMW (733051106); France, Agence Nationale de la Recherche (ANR-19-JPW2–000); Italy, Italian Ministry of Health (MoH); Australia, National Health & Medical Research Council (1191535); Hungary, National Research, Development and Innovation Office (2019–2.1.7-ERA-NET-2020–00008). This study was also supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre and EPSRC grant EP/M020533/1. A.L.Y is supported by a Skills Development Fellowship from the Medical Research Council (MR/T027800/1). F.B. is a National Institute of Health Research (NIHR) Professor. N.P.O. is a UKRI Future Leaders Fellow (MR/S03546X/1). A.A. holds an MRC eMedLab Medical Bioinformatics Career Development Fellowship. This work was partly supported by the Medical Research Council ( MR/L016311/1 ].
Publisher Copyright:
© 2023 The Author(s)
PY - 2023/5/1
Y1 - 2023/5/1
N2 - 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.
AB - 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.
KW - Alzheimer's disease
KW - Early risk factors
KW - Modelling
KW - Subtypes
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85150259051&origin=inward
UR - https://www.ncbi.nlm.nih.gov/pubmed/36907283
U2 - 10.1016/j.neuroimage.2023.120005
DO - 10.1016/j.neuroimage.2023.120005
M3 - Article
C2 - 36907283
SN - 1053-8119
VL - 271
JO - NeuroImage
JF - NeuroImage
M1 - 120005
ER -