Analysis of disease comorbidity patterns in a large-scale China population

Abstract

BackgroundDisease comorbidity is popular and has significant indications for disease progress and management. We aim to detect the general disease comorbidity patterns in Chinese populations using a large-scale clinical data set.MethodsWe extracted the diseases from a large-scale anonymized data set derived from 8,572,137 inpatients in 453 hospitals across China. We built a Disease Comorbidity Network (DCN) using correlation analysis and detected the topological patterns of disease comorbidity using both complex network and data mining methods. The comorbidity patterns were further validated by shared molecular mechanisms using disease-gene associations and pathways. To predict the disease occurrence during the whole disease progressions, we applied four machine learning methods to model the disease trajectories of patients.ResultsWe obtained the DCN with 5702 nodes and 258,535 edges, which shows a power law distribution of the degree and weight. It further indicated that there exists high heterogeneity of comorbidities for different diseases and we found that the DCN is a hierarchical modular network with community structures, which have both homogeneous and heterogeneous disease categories. Furthermore, adhering to the previous work from US and Europe populations, we found that the disease comorbidities have their shared underlying molecular mechanisms. Furthermore, take hypertension and psychiatric disease as instance, we used four classification methods to predicte the disease occurrence using the comorbid disease trajectories and obtained acceptable performance, in which in particular, random forest obtained an overall best performance (with F1-score 0.6689 for hypertension and 0.6802 for psychiatric disease).ConclusionsOur study indicates that disease comorbidity is significant and valuable to understand the disease incidences and their interactions in real-world populations, which will provide important insights for detection of the patterns of disease classification, diagnosis and prognosis.

Comorbidity Networks in Cardiovascular Diseases

“…Recently, network modularity studies have been used to unveil clinically relevant comorbidity patterns (Barabási et al, 2011 ; Divo et al, 2015 ; Choi et al, 2017 ; Guo et al, 2019 ). In a similar way to cluster analysis is to assign diseases into modules or communities, so that nodes in the same community are strongly associated with one another than entities from different clusters.…”

Deep learning in systems medicine

“…The increasing availability of large clinical datasets and medical insurance data with diagnosis and treatment details opened the opportunity to map diseases comorbidities [116].…”

“…The increasing availability of large clinical datasets and medical insurance data with diagnosis and treatment details opened the opportunity to map diseases comorbidities [ 115 ]. One of the most interesting papers, which are a motivation behind this work, is the human disease network [ 116 ] in which a scalable DL approach was adopted to forecasting disease trajectories over time.…”

LeMeDISCO: A computational method for large-scale prediction & molecular interpretation of disease comorbidity

“…Interestingly, rheumatoid arthritis, autoimmune thyroiditis, and insulin-dependent diabetes mellitus cooccur, but rheumatoid arthritis and multiple sclerosis do not 1 . Previously, there have been several efforts to investigate the molecular features responsible for human disease comorbidities [2][3][4][5][6][7][8] . Some studies focused on particular subsets of diseases 3 or ethnic groups, while others investigated the entire human disease network [4][5][6][7] .…”