Abstract
In “classic” biomedical research, diseases have usually been studied individually. The pioneering human disease network (HDN) studies jointly consider a large number of diseases, analyse their interconnections, and provide a more comprehensive description of diseases. However, most of the existing HDN studies are based on molecular information and can only partially describe disease interconnections. Building on the unique Taiwan National Health Insurance Research Database (NHIRD), in this study, we construct the epidemiological HDN (eHDN), where two diseases are concluded as interconnected if their observed probability of co-occurrence deviating that expected under independence. Advancing from the existing HDN, the eHDN can also accommodate non-molecular connections and have more important practical implications. Building on the network construction, we examine important network properties such as connectivity, module, hub, and others and describe their temporal patterns. This study is among the first to systematically construct the eHDN and can have important implications for human disease research and health care and management.
Highlights
Promising findings have been made in the human disease network (HDN) and other pan-disease studies
For a more intuitive description of the patient-disease distributions, in Fig. S2 in Supplementary Information (SI), we present the patient-disease heatmaps, where the x-axis corresponds to patients, the y-axis corresponds to diseases, and a red dot represents one disease occurrence
Different from the existing studies that are based on molecular information, in this study, we have taken advantage of the unique National Health Insurance Research Database (NHIRD), constructed the epidemiological HDN (eHDN) co-occurrence network, and studied its properties
Summary
Promising findings have been made in the HDN and other pan-disease studies. Notable studies include Calvano et al, which explored the genome-wide interaction network and suggested that network analysis using comprehensive knowledge can identify new functional modules perturbed in the disease processes[7]. The goal of this study is to construct the epidemiological HDN (eHDN), where two diseases are concluded as connected if their probability of co-occurring in clinics deviating from that expected under independence This effort will take advantage of the unique Taiwan National Health Insurance Research Database (NHIRD; more details below). With the huge sample size of NHIRD, the constructed network can be more reliable than some of the existing ones Overall, this eHDN analysis may complement the existing molecular HDNs and significantly advance our understanding of disease interconnections from an epidemiological perspective. This eHDN analysis may complement the existing molecular HDNs and significantly advance our understanding of disease interconnections from an epidemiological perspective It may provide important insights for health care and management
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