Background: Alzheimer’s disease (AD) and related dementias are becoming an epidemic and there is an urgent need to develop effective therapies to prevent or delay onset. However, clinical trials to date have failed to find an effective drug even though there is some evidence of adequate target engagement in many studies, but without a corresponding clinical benefit. One reason for these failures may be that multiple co-pathologies, including neurodegenerative causes together with cerebrovascular disease, underlie the more common sporadic forms of dementia. Thus drugs used in trials targeting a single neuropathological entity, such as beta-amyloid, do not cover the full spectrum of underlying pathology.
Overall goal: To leverage large, multimodal datasets from patients representing the full spectrum of
neurodegenerative dementia as well as those at risk and apply well-informed data-driven analytic approaches to identify neurodegenerative dementia spectra and biotypes that can discern between pure from mixed forms of dementia, i.e., that represent the true complexity of the underlying pathologies, which does not rely on original clinical diagnosis.
Study Design: We will combine multivariate, heterogeneous, and multimodal genomic, neuroimaging, cognitive/behavioural, biomarker and demographic datasets from two Canadian dementia cohorts, a Czech dementia cohort, an Italian dementia cohort, including the full spectrum of pathologies associated with dementia, and a longitudinal aging cohort (Rotterdam Study) to achieve this goal. Dementia biotypes will also be defined using a variety of data-driven analytic approaches developed by the expertise of our neuroinformatics team. These dementia spectra will also be applied to the aging cohorts to see how they predict development of dementia/cognitive impairment and which specific biotype, i.e., presymptomatic disease signatures. Validation will take place using an autopsy subset of the data.
Significance: Understanding shared mechanisms leading to this underlying pathological complexity in late onset sporadic dementia represent a critical knowledge gap and may inform future clinical trial design and more appropriate patient selection a priori (i.e., applied precision medicine approaches).

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