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Published on: May 20, 2024
Homogeneous clusters of Alzheimer's disease patient population
Dragan Gamberger1, Bernard Ženko2, Alexis Mitelpunkt3
1Rudjer Bošković Institute, Bijenička 54, 10000, Zagreb, Croatia. dragan.gamberger@irb.hr.
Insights
This study identified three distinct Alzheimer's disease (AD) patient clusters using novel clustering tools. Brain atrophy drives dementia, but one cluster shows large brain volumes, suggesting trauma and degeneration may contribute to AD progression.
Area of Science:
- Neuroscience
- Medical Data Analysis
Background:
- Identifying Alzheimer's disease (AD) biomarkers presents significant challenges in medical research and data analysis.
- Existing methods struggle to differentiate AD patient subpopulations effectively.
Purpose of the Study:
- To apply a novel clustering tool for identifying homogeneous subpopulations within AD patients.
- To analyze clinical and biological descriptors for AD patient stratification.
Main Methods:
- Utilized a novel clustering algorithm to analyze patient data.
- Grouped patients based on homogeneous clinical and biological characteristics.
Main Results:
- Identified three distinct patient clusters with significant dementia-related problems.
- Confirmed brain atrophy as a primary driver of dementia.
- Discovered an unexpected subpopulation with significant dementia, mild brain atrophy, and enlarged brain volumes, potentially linked to trauma and degeneration, predominantly in males.
Conclusions:
- Findings have implications for Alzheimer's disease research and clinical trial design.
- The employed clustering methodology offers potential applications in other medical and biological fields.
Background:
Identification of biomarkers for the Alzheimer's disease (AD) is a challenge and a very difficult task both for medical research and data analysis.
Methods:
We applied a novel clustering tool with the goal to identify subpopulations of the AD patients that are homogeneous in respect of available clinical as well as in respect of biological descriptors.
Results:
The main result is identification of three clusters of patients with significant problems with dementia. The evaluation of properties of these clusters demonstrates that brain atrophy is the main driving force of dementia. The unexpected result is that the largest subpopulation that has very significant problems with dementia has besides mild signs of brain atrophy also large ventricular, intracerebral and whole brain volumes. Due to the fact that ventricular enlargement may be a consequence of brain injuries and that a large majority of patients in this subpopulation are males, a potential hypothesis is that such medical status is a consequence of a combination of previous traumatic events and degenerative processes.
Conclusions:
The results may have substantial consequences for medical research and clinical trial design. The clustering methodology used in this study may be interesting also for other medical and biological domains.
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