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

Abstract

Background: Disease 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.

Methods: We 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.

Results: We 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).

Conclusions: Our 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.

Keywords: Complex network; Disease comorbidity; Network medicine.

Fig. 1

The framework to predict disease occurrence using the comorbid trajectories of patients

Fig. 2

Basic properties of the network. a Distribution of degree. b Weight distribution of edges. c Distribution of CC₁. d Distribution of BC. e Distribution of CC₂. f The top 10 diseases with the highest degree, CC₂ and BC, respectively

Fig. 3

The relationship between topological properties and the network structure. a Degree and CC₁; b CC₂ and CC₁; c Degree and CC₂; d BC and CC₂; e Degree and BC; f CC₁ and BC; g Modules in the network; h One specific disease comorbidity module in the network

Fig. 4

The shared molecular mechanisms of disease comorbidity. a The relationship between shared genes and intensity of disease comorbidity b. The relationship between shared pathways and intensity of disease comorbidity c. Disease comorbidity of Alzheimer’s Disease and Arteriosclerotic Heart Disease