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Hypergraph Clustering for Analyzing Chronic Disease Patterns in Mild Cognitive Impairment Reversion and Progression
Background:
Limited research has explored the preceding sequences of medical conditions leading to mild cognitive impairment (MCI), particularly patterns that may signal progression to dementia or reversion to normal cognition.
Objectives:
Our study aims to analyze common sequences of chronic conditions preceding an MCI diagnosis and examine differences between retention in MCI or progression towards dementia and reversion to normal cognition, using hypergraph clustering, a network analysis approach.
Methods:
We categorize participants into two groups (i) M2P: stay or progressed to dementia, or (ii) M2N: reversion to normal within 5 years after the first onset of MCI. Among 414 participants, 210 are males and the mean age is 80.8 years. We performed network analysis to obtain categories and sequences of chronic conditions. We applied hypergraph spectral clustering to characterize participants with similar sequences.
Results Results:
We identify and validate the generic key indicators (e.g., chronic kidney disease), highlight the sex-specific potential indicators (e.g., arthritis) for MCI reversal, and open new research directions for identifying potential disparities among men and women.
Conclusion:
We categorized the chronic conditions preceding MCI diagnosis and discovered unique sequences suggesting MCI reversion among men and women to facilitate future research.
Insights
Understanding chronic conditions preceding mild cognitive impairment (MCI) is key. This study reveals unique condition sequences that may predict MCI reversion or dementia progression in men and women.
Area of Science:
- Gerontology
- Neurology
- Network Science
Background:
- Limited research exists on the chronic condition sequences preceding mild cognitive impairment (MCI).
- Identifying patterns that predict dementia progression or reversion to normal cognition is crucial.
Purpose of the Study:
- To analyze common chronic condition sequences before MCI diagnosis.
- To differentiate sequences associated with MCI progression versus reversion to normal cognition.
- To explore sex-specific differences in these sequences.
Main Methods:
- Utilized hypergraph clustering, a network analysis approach.
- Categorized participants into MCI-to-progression (M2P) or MCI-to-normal (M2N) groups within 5 years.
- Analyzed sequences of chronic conditions in 414 participants (mean age 80.8 years, 210 males).
Main Results:
- Identified generic key indicators for MCI, such as chronic kidney disease.
- Highlighted sex-specific indicators, like arthritis, potentially linked to MCI reversal.
- Revealed distinct condition sequences associated with MCI outcomes.
Conclusions:
- Chronic condition sequences preceding MCI diagnosis were categorized.
- Unique sequences suggesting MCI reversion were discovered for men and women.
- Findings facilitate future research into MCI disparities and potential interventions.
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