Mapping multimorbidity progression among 190 diseases.

ABSTRACT

BACKGROUND: Current clustering of multimorbidity based on the frequency of common disease combinations is inadequate. We estimated the causal relationships among prevalent diseases and mapped out the clusters of multimorbidity progression among them. METHODS: In this cohort study, we examined the progression of multimorbidity among 190 diseases among over 500,000 UK Biobank participants over 12.7 years of follow-up. Using a machine learning method for causal inference, we analyzed patterns of how diseases influenced and were influenced by others in females and males. We used clustering analysis and visualization algorithms to identify multimorbidity progress constellations. RESULTS: We show the top influential and influenced diseases largely overlap between sexes in chronic diseases, with sex-specific ones tending to be acute diseases. Patterns of diseases that influence and are influenced by other diseases also emerged (clustering significance Pau > 0.87), with the top influential diseases affecting many clusters and the top influenced diseases concentrating on a few, suggesting that complex mechanisms are at play for the diseases that increase the development of other diseases while share underlying causes exist among the diseases whose development are increased by others. Bi-directional multimorbidity progress presents substantial clustering tendencies both within and across International Classification Disease chapters, compared to uni-directional ones, which can inform future studies for developing cross-specialty strategies for multimorbidity. Finally, we identify 10 multimorbidity progress constellations for females and 9 for males (clustering stability, adjusted Rand index >0.75), showing interesting differences between sexes. CONCLUSION: Our findings could inform the future development of targeted interventions and provide an essential foundation for future studies seeking to improve the prevention and management of multimorbidity.

Mapping out clusters of diseases is crucial to addressing the rising challenge of co-occurrence of multiple diseases, known as multimorbidity. However, the current way of grouping diseases based on their associations isn't enough to understand how they develop over time. We've come up with a new approach to map out how groups of diseases progress together based on the strength of their causal relationships. By looking at how each disease affects the development of others, we can get a better understanding of how they form clusters. Our research goes beyond just showing which diseases occur together, and it's a step toward improving how we prevent and manage multiple health conditions in the future.