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Biological classification of memory clinic patients
Sophie E Mastenbroek1,2,3, Lyduine E Collij1,2,3, Toomas Erik Anijärv1
1Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Lund 222 42, Sweden.
Brain : a Journal of Neurology
|October 29, 2025
Summary
A new biomarker framework categorizes memory clinic patients into five profiles: Alzheimer's disease, α-Synuclein disease, vascular disease, mixed, and a novel group (NOVAS). This approach aids diagnosis and predicts disease progression for neurodegenerative conditions.
Area of Science:
- Neuroscience
- Biomarker Discovery
- Clinical Neurology
Background:
- Traditional neurodegenerative disease definitions rely on clinical symptoms.
- Biomarker development enables in vivo biological definitions.
- Characterizing memory clinic populations requires multi-dimensional biomarker data.
Purpose of the Study:
- To develop a data-driven biological framework for categorizing memory clinic patients.
- To identify distinct clinico-pathological entities using multimodal biomarkers.
- To compare clinical symptoms, progression, and brain changes across identified profiles.
Main Methods:
- Studied 1,677 individuals (SCD, MCI, dementia, controls) from the BioFINDER-2 cohort.
- Applied the Subtype and Stage Inference (SuStaIn) model to biomarkers: Aβ, tau PET, α-synuclein SAA, WMHs, and cortical thickness.
- Analyzed clinical symptoms, disease progression, and brain changes (atrophy, functional connectivity) in symptomatic individuals.
Main Results:
- Identified five biomarker clusters: Alzheimer's disease (AD), α-Synuclein disease (αSyn), Vascular disease, Mixed AD/Vascular, and NOVAS.
- AD profile showed global cognitive decline; αSyn profile exhibited visuospatial/executive dysfunction and motor impairment.
- Vascular and Mixed profiles indicated language/motor impairments and subcortical atrophy; NOVAS showed hippocampal/amygdala atrophy, potentially representing other pathologies.
Conclusions:
- Multimodal biomarker classification provides valuable diagnostic and prognostic insights for neurodegenerative diseases.
- The identified profiles correlate with distinct clinical trajectories and underlying pathologies.
- This framework has potential implications for optimizing clinical trial design and patient stratification.
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