Original Title: Biomarkers
Journal: Alzheimer's & dementia : the journal of the Alzheimer's Association
Overview
Alzheimer’s disease is a heterogeneous condition often occurring alongside cerebral small vessel disease. This study examines 262 individuals across two cohorts: the longitudinal TRIAD cohort, representing a low burden of small vessel disease, and the MITNEC-C6 cohort, which includes real-world patients with mixed dementia and moderate-to-severe vascular lesions. Using a deep learning segmentation tool and the Subtype and Stage Inference algorithm, the research team identified distinct imaging-derived subtypes based on amyloid deposition, white matter hyperintensities, perivascular spaces, and diffusion markers. The study tracked 202 individuals at baseline, with follow-ups at two and three years. The primary goal was to determine if amyloid levels or free-water metrics could predict the progression of vascular lesions within specific disease subtypes over time.
Novelty
The study introduces a data-driven approach to categorize patients by applying the SuStaIn algorithm to amyloid PET and multi-modal MRI markers. It identifies three distinct patterns: vascular-first, amyloid-first, and mixed subtypes. The amyloid-first subtype was only present in the low-vascular burden cohort, while the vascular-first and mixed subtypes appeared in both groups. The research demonstrates that baseline amyloid levels predict faster growth of white matter hyperintensities in the vascular-first subtype (beta=0.03±0.010, P=0.002). Furthermore, in the mixed subtype, higher baseline free-water levels, rather than amyloid, predicted the expansion of white matter hyperintensities (beta=0.14±0.04, P<0.001). This distinction highlights that the drivers of vascular injury progression vary significantly depending on the underlying disease subtype.
Potential Clinical / Research Applications
These findings have implications for clinical trial design and personalized medicine. Using the SuStaIn algorithm, clinicians could screen patients to identify which pathological driver—amyloid or vascular—is most active. For instance, individuals in the vascular-first subtype might benefit more from amyloid-lowering therapies to slow down secondary vascular damage (P=0.002). Conversely, for the mixed subtype, monitoring free-water levels could provide a window for early intervention before significant white matter hyperintensity growth occurs. In research, these imaging-derived subtypes offer a framework for selecting homogeneous patient groups, which might increase the statistical power of studies investigating new therapies. This approach moves toward a nuanced understanding of dementia that accounts for the frequent overlap of different disease processes.
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