Data-driven subtypes of Alzheimer's Disease: A multicohort study
- 1McGill University
Abstract
INTRODUCTIONIndividuals with Alzheimers disease (AD) exhibit substantial heterogeneity in the severity of vascular changes and neurodegeneration, making the identification of biologically meaningful subtypes across the AD spectrum critical for improving prognosis and tailoring interventions. We aimed to determine whether data-driven subtyping of individuals at risk for or diagnosed with AD, based on magnetic resonance imaging (MRI), can identify distinct trajectories of disease progression and clinical outcomes.
METHODSWe analyzed baseline biomarkers and longitudinal clinical and cognitive assessments from the PREVENT-AD, CIMA-Q, ALLFTD, and ADNI cohorts, including 1396 participants. Regional atrophy and White Matter Hyperintensity (WMH) burden were derived from T1-weighted MRI. The SuStaIn algorithm was applied to baseline data to infer individuals subtypes and stages based on MRI biomarker profiles. The resulting subtypes were compared in terms of baseline molecular biomarker levels and vascular risk factors, longitudinal cognitive decline trajectories, and clinical and neuropathological outcomes.
RESULTSTwo distinct subtypes were identified across all cohorts: one characterized by early parahippocampal and cortical atrophy, and a second by initially elevated WMH volumes. Both subtypes showed abnormal tau and amyloid levels in- and ex-vivo, and subtype two exhibited increased vascular risk factors. Longitudinally, the atrophy-first subtype exhibited steeper cognitive decline than the WMH-first subtype. Accordingly, the atrophy-first subtype carried a higher risk of conversion to AD than the WMH-first group.
DISCUSSIONThese findings, replicated in four diverse and independent datasets, support imaging-based subtyping as a scalable and clinically translatable approach for capturing heterogeneity in AD pathophysiology and associated cognitive trajectories.
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BibTeX
@misc{metz2026datadriven,
title = {{Data-driven subtypes of Alzheimer's Disease: A multicohort study}},
author = {A. Metz and C. Polizzi and R. Moqadam and K. Chadwick and C. Tremblay and Z. Chen and Y. Zeighami and S. Villeneuve and Alzheimer's Disease Neuroimaging Initiative (ADNI) and ARTFL-LEFFTDS Longitudinal Frontotemporal Lobar Degeneration (ALLFTD) Research Consortium and Consortium for the early identification of Alzheimer's disease - Quebec (CIMA-Q) and The PREVENT-AD Research Group and M. Dadar},
howpublished = {medRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.09.26362683},
url = {https://www.medrxiv.org/content/10.64898/2026.09.09.26362683v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Data-driven subtypes of Alzheimer's Disease: A multicohort study
AU - A. Metz
AU - C. Polizzi
AU - R. Moqadam
AU - K. Chadwick
AU - C. Tremblay
AU - Z. Chen
AU - Y. Zeighami
AU - S. Villeneuve
AU - Alzheimer's Disease Neuroimaging Initiative (ADNI)
AU - ARTFL-LEFFTDS Longitudinal Frontotemporal Lobar Degeneration (ALLFTD) Research Consortium
AU - Consortium for the early identification of Alzheimer's disease - Quebec (CIMA-Q)
AU - The PREVENT-AD Research Group
AU - M. Dadar
PY - 2026
DA - 2026/09/09
PB - medRxiv
DO - 10.64898/2026.09.09.26362683
UR - https://www.medrxiv.org/content/10.64898/2026.09.09.26362683v1
AB - INTRODUCTIONIndividuals with Alzheimers disease (AD) exhibit substantial heterogeneity in the severity of vascular changes and neurodegeneration, making the identification of biologically meaningful subtypes across the AD spectrum critical for improving prognosis and tailoring interventions. We aimed to determine whether data-driven subtyping of individuals at risk for or diagnosed with AD, based on magnetic resonance imaging (MRI), can identify distinct trajectories of disease progression and clinical outcomes. METHODSWe analyzed baseline biomarkers and longitudinal clinical and cognitive assessments from the PREVENT-AD, CIMA-Q, ALLFTD, and ADNI cohorts, including 1396 participants. Regional atrophy and White Matter Hyperintensity (WMH) burden were derived from T1-weighted MRI. The SuStaIn algorithm was applied to baseline data to infer individuals subtypes and stages based on MRI biomarker profiles. The resulting subtypes were compared in terms of baseline molecular biomarker levels and vascular risk factors, longitudinal cognitive decline trajectories, and clinical and neuropathological outcomes. RESULTSTwo distinct subtypes were identified across all cohorts: one characterized by early parahippocampal and cortical atrophy, and a second by initially elevated WMH volumes. Both subtypes showed abnormal tau and amyloid levels in- and ex-vivo, and subtype two exhibited increased vascular risk factors. Longitudinally, the atrophy-first subtype exhibited steeper cognitive decline than the WMH-first subtype. Accordingly, the atrophy-first subtype carried a higher risk of conversion to AD than the WMH-first group. DISCUSSIONThese findings, replicated in four diverse and independent datasets, support imaging-based subtyping as a scalable and clinically translatable approach for capturing heterogeneity in AD pathophysiology and associated cognitive trajectories.
ER -