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Mapping multimorbidity progression among 190 diseases.

Shasha Han1,2,3, Sairan Li4, Yunhaonan Yang5

  • 1School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China. hanshasha@pumc.edu.cn.

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This study reveals distinct patterns in how diseases progress together, identifying specific multimorbidity constellations in males and females. These findings can guide better prevention and management strategies for multiple chronic conditions.

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Area of Science:

  • Multimorbidity research
  • Causal inference in chronic diseases
  • Network analysis of disease progression

Background:

  • Current methods for clustering multimorbidity are insufficient.
  • Need for understanding causal relationships and progression patterns.

Purpose of the Study:

  • To estimate causal relationships among prevalent diseases.
  • To map clusters of multimorbidity progression.
  • To identify sex-specific patterns in disease co-occurrence.

Main Methods:

  • Cohort study of over 500,000 UK Biobank participants.
  • Analysis of 190 diseases over 12.7 years.
  • Machine learning for causal inference and clustering analysis.

Main Results:

  • Identified significant disease progression patterns and constellations.
  • Top influential and influenced diseases largely overlap between sexes for chronic conditions.
  • Found substantial clustering in bi-directional multimorbidity progress.
  • Identified 10 constellations for females and 9 for males, with sex-specific differences.

Conclusions:

  • Findings can inform targeted interventions for multimorbidity.
  • Provides a foundation for improving multimorbidity prevention and management.
  • Highlights the need for cross-specialty strategies.