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 chro

SUBMITTER: Han S 

PROVIDER: S-EPMC11239867 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature