Data-driven subtypes of Alzheimer's Disease: A multicohort study

Individuals with Alzheimer's 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.

We 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.

Two 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.

These 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.