Hypergraph Clustering for Analyzing Chronic Disease Patterns in Mild Cognitive Impairment Reversion and Progression

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.