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Tau topography subtypes account for clinical heterogeneity and longitudinal trajectories in early-onset Alzheimer's disease

Lin, Marlene ; Chiotis, Konstantinos ; Maiti, Piyush ; Zhang, Jiaxiuxiu ; Blazhenets, Ganna ; Rocha, Salma ; Shankar, Ranjani ; Amuiri, Alinda ; Hammers, Dustin B. and Eloyan, Ani , et al. (2026) In Brain Communications 8(3).
Abstract

The growing availability of large-scale biomarker datasets has allowed data-driven methods to characterize Alzheimer’s disease biological heterogeneity. However, most prior studies have focused on cohorts of late-onset amnestic cases, leaving early-onset Alzheimer’s disease underexplored. We aimed to characterize tau-PET-based subtypes through a robust data-driven approach in the Longitudinal Early-Onset Alzheimer’s Disease Study. Baseline [18F]Flortaucipir PET scans from 365 amyloid-PET-positive participants with sporadic early-onset Alzheimer’s disease were quantified in the left and right medial temporal, lateral temporal, occipital, parietal, and frontal cortices. Tau PET values were z-scored against 85... (More)

The growing availability of large-scale biomarker datasets has allowed data-driven methods to characterize Alzheimer’s disease biological heterogeneity. However, most prior studies have focused on cohorts of late-onset amnestic cases, leaving early-onset Alzheimer’s disease underexplored. We aimed to characterize tau-PET-based subtypes through a robust data-driven approach in the Longitudinal Early-Onset Alzheimer’s Disease Study. Baseline [18F]Flortaucipir PET scans from 365 amyloid-PET-positive participants with sporadic early-onset Alzheimer’s disease were quantified in the left and right medial temporal, lateral temporal, occipital, parietal, and frontal cortices. Tau PET values were z-scored against 85 amyloid-PET-negative cognitively normal age-matched participants and fitted into Subtype and Stage Inference (SuStaIn)—an unsupervised clustering algorithm that simultaneously models subtypes and progression from cross-sectional data. The derived subtypes were subsequently characterized by baseline and longitudinal clinical, cognitive, MRI, tau and amyloid PET features. We identified three tau-PET-based subtypes: on average, Subtype 1/Typical (n = 144, 40%) showed a predominant bilateral temporoparietal pattern typical of Alzheimer’s disease. Subtype 2/Left temporal (n = 111, 31%) showed predominant left temporal binding. Subtype 3/Posterior (n = 104, 29%) showed early and permeating occipitoparietal involvement. Subtypes did not differ in demographics or global amyloid burden, but were relatively more enriched for specific clinical presentations: S1/Typical for amnestic presentations, S2/Left Temporal for primary progressive aphasia, and S3/Posterior for posterior cortical atrophy. Baseline tau PET subtypes aligned with cortical atrophy patterns and domain-specific cognitive impairment. When follow-up tau PET scans were fitted to SuStaIn trained on baseline data, 85.6% (n = 172/201) of participants retained the same subtype classification, indicating subtype temporal stability, and progressed within subtypes by 0.56 ± 0.70 SuStaIn stage/year. Longitudinal voxel-wise linear mixed-effects modelling revealed tau accumulation patterns for each subtype in regions relatively spared at baseline: occipital lobe accumulation predominated in S1/Typical, bilateral frontal and right temporal in S2/Left Temporal, and bilateral frontotemporal lobes in S3/Posterior. All subtypes showed longitudinal increases in Clinical Dementia Rating-Sum of Boxes, but with slower worsening in S3/Posterior compared with the other subtypes. Our findings reveal robust subtypes in sporadic early-onset Alzheimer’s disease characterized by distinct spatiotemporal tau patterns that parallel differences in clinical presentations and trajectories of neurodegeneration. These subtypes extend beyond traditional clinical syndromes and support a more nuanced framework for individualized prognosis and care. Incorporating tau PET subtyping into clinical trial design could enable more targeted therapeutic approaches for this younger population.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
atypical Alzheimer's disease, data-driven clustering, disease heterogeneity, neuroimaging biomarkers, voxel-wise modelling
in
Brain Communications
volume
8
issue
3
article number
fcag176
publisher
Oxford University Press
external identifiers
  • scopus:105040948926
  • pmid:42255923
ISSN
2632-1297
DOI
10.1093/braincomms/fcag176
language
English
LU publication?
yes
id
8ddd93d1-6e83-4180-87c5-c78d13a1d5b7
date added to LUP
2026-09-16 10:12:43
date last changed
2026-09-17 03:00:06
@article{8ddd93d1-6e83-4180-87c5-c78d13a1d5b7,
  abstract     = {{<p>The growing availability of large-scale biomarker datasets has allowed data-driven methods to characterize Alzheimer’s disease biological heterogeneity. However, most prior studies have focused on cohorts of late-onset amnestic cases, leaving early-onset Alzheimer’s disease underexplored. We aimed to characterize tau-PET-based subtypes through a robust data-driven approach in the Longitudinal Early-Onset Alzheimer’s Disease Study. Baseline [<sup>18</sup>F]Flortaucipir PET scans from 365 amyloid-PET-positive participants with sporadic early-onset Alzheimer’s disease were quantified in the left and right medial temporal, lateral temporal, occipital, parietal, and frontal cortices. Tau PET values were z-scored against 85 amyloid-PET-negative cognitively normal age-matched participants and fitted into Subtype and Stage Inference (SuStaIn)—an unsupervised clustering algorithm that simultaneously models subtypes and progression from cross-sectional data. The derived subtypes were subsequently characterized by baseline and longitudinal clinical, cognitive, MRI, tau and amyloid PET features. We identified three tau-PET-based subtypes: on average, Subtype 1/Typical (n = 144, 40%) showed a predominant bilateral temporoparietal pattern typical of Alzheimer’s disease. Subtype 2/Left temporal (n = 111, 31%) showed predominant left temporal binding. Subtype 3/Posterior (n = 104, 29%) showed early and permeating occipitoparietal involvement. Subtypes did not differ in demographics or global amyloid burden, but were relatively more enriched for specific clinical presentations: S1/Typical for amnestic presentations, S2/Left Temporal for primary progressive aphasia, and S3/Posterior for posterior cortical atrophy. Baseline tau PET subtypes aligned with cortical atrophy patterns and domain-specific cognitive impairment. When follow-up tau PET scans were fitted to SuStaIn trained on baseline data, 85.6% (n = 172/201) of participants retained the same subtype classification, indicating subtype temporal stability, and progressed within subtypes by 0.56 ± 0.70 SuStaIn stage/year. Longitudinal voxel-wise linear mixed-effects modelling revealed tau accumulation patterns for each subtype in regions relatively spared at baseline: occipital lobe accumulation predominated in S1/Typical, bilateral frontal and right temporal in S2/Left Temporal, and bilateral frontotemporal lobes in S3/Posterior. All subtypes showed longitudinal increases in Clinical Dementia Rating-Sum of Boxes, but with slower worsening in S3/Posterior compared with the other subtypes. Our findings reveal robust subtypes in sporadic early-onset Alzheimer’s disease characterized by distinct spatiotemporal tau patterns that parallel differences in clinical presentations and trajectories of neurodegeneration. These subtypes extend beyond traditional clinical syndromes and support a more nuanced framework for individualized prognosis and care. Incorporating tau PET subtyping into clinical trial design could enable more targeted therapeutic approaches for this younger population.</p>}},
  author       = {{Lin, Marlene and Chiotis, Konstantinos and Maiti, Piyush and Zhang, Jiaxiuxiu and Blazhenets, Ganna and Rocha, Salma and Shankar, Ranjani and Amuiri, Alinda and Hammers, Dustin B. and Eloyan, Ani and Kirby, Kala and Koeppe, Robert A. and Aisen, Paul and Beckett, Laurel and Kukull, Walter A. and Toga, Arthur W. and Atri, Alireza and Clark, David G. and Day, Gregory S. and Duara, Ranjan and Graff-Radford, Neill R. and Grant, Ian and Honig, Lawrence S. and Johnson, Erik C.B. and Jones, David T. and Masdeu, Joseph C. and Mendez, Mario F. and Musiek, Erik and Onyike, Chiadi U. and Riddle, Meghan and Rogalski, Emily and Salloway, Stephen and Sha, Sharon J. and Turner, Raymond Scott and Wingo, Thomas S. and Wolk, David A. and Womack, Kyle B. and Nudelman, Kelly Nicole Holohan and Touroutoglou, Alexandra and Jack, Clifford R. and Vemuri, Prashanthi and Vogel, Jacob W. and Dufault, Suzanne M. and Hoffmann, Thomas J. and Carrillo, Maria C. and Dickerson, Bradford C. and Apostolova, Liana G. and Rabinovici, Gil D. and La Joie, Renaud}},
  issn         = {{2632-1297}},
  keywords     = {{atypical Alzheimer's disease; data-driven clustering; disease heterogeneity; neuroimaging biomarkers; voxel-wise modelling}},
  language     = {{eng}},
  number       = {{3}},
  publisher    = {{Oxford University Press}},
  series       = {{Brain Communications}},
  title        = {{Tau topography subtypes account for clinical heterogeneity and longitudinal trajectories in early-onset Alzheimer's disease}},
  url          = {{http://dx.doi.org/10.1093/braincomms/fcag176}},
  doi          = {{10.1093/braincomms/fcag176}},
  volume       = {{8}},
  year         = {{2026}},
}