Ajou University School of Medicine, Department of Biomedical Informatics, Suwon, 16499, Republic of Korea.
Medstar Georgetown University Hospital, Department of Pathology, Washington, DC, 20007, USA.
Sci Rep. 2017 Nov 14;7(1):15561. doi: 10.1038/s41598-017-15647-4.
In recent years, several network models have been introduced to elucidate the relationships between diseases. However, important risk factors that contribute to many human diseases, such as age, gender and prior diagnoses, have not been considered in most networks. Here, we construct a diagnosis progression network of human diseases using large-scale claims data and analyze the associations between diagnoses. Our network is a scale-free network, which means that a small number of diagnoses share a large number of links, while most diagnoses show limited associations. Moreover, we provide strong evidence that gender, age and disease class are major factors in determining the structure of the disease network. Practically, our network represents a methodology not only for identifying new connectivity that is not found in genome-based disease networks but also for estimating directionality, strength, and progression time to transition between diseases considering gender, age and incidence. Thus, our network provides a guide for investigators for future research and contributes to achieving precision medicine.
近年来，已经有几种网络模型被引入来阐明疾病之间的关系。然而，在大多数网络中，并没有考虑到导致许多人类疾病的重要风险因素，如年龄、性别和既往诊断。在这里，我们使用大规模的索赔数据构建了人类疾病的诊断进展网络，并分析了诊断之间的关联。我们的网络是一个无标度网络，这意味着少数诊断具有大量的联系，而大多数诊断显示出有限的关联。此外，我们提供了强有力的证据表明，性别、年龄和疾病类别是决定疾病网络结构的主要因素。实际上，我们的网络不仅代表了一种方法，可以识别在基于基因组的疾病网络中找不到的新连接性，还代表了一种方法，可以估计在考虑性别、年龄和发病率的情况下，疾病之间的转移方向、强度和进展时间。因此，我们的网络为未来的研究提供了一个指导方向，有助于实现精准医学。
