Chronic lifestyle diseases display seasonal sensitive comorbid trend in human population evidence from Google Trends

Seasonal and human physiological changes are important factors in the development of many diseases. But, the study of genuine seasonal impact on these diseases is difficult to measure due to many other environment and lifestyle factors which directly affect these diseases. However, several clinical studies have been conducted in different parts of the world, and it has clearly indicated that certain groups of population are highly subjected to seasonal changes, and their maladaptation can possibly lead to several disorders/diseases. Thus, it is crucial to study the significant seasonal sensitive diseases spread across the human population. To narrow down these disorders/diseases, the study hypothesized that high altitude (HA) associated diseases and disorders are of the strong variants of seasonal physiologic changes. It is because, HA is the only geographical condition for which humans can develop very efficient physiological adaptation mechanism called acclimatization. To study this hypothesis, PubMed was used to collect the HA associated symptoms and disorders. Disease Ontology based semantic similarity network (DSN) and disease-drug networks were constructed to narrow down the benchmark diseases and disorders of HA. The DSN which was further subjected to different community structure analysis uncovered the highly associated or possible comorbid diseases of HA. The predicted 12 lifestyle diseases were assumed to be “seasonal (sensitive) comorbid lifestyle diseases (SCLD)”. A time series analyses on Google Search data of the world from 2004–2016 was conducted to investigate whether the 12 lifestyle diseases have seasonal patterns. Because, the trends were sensitive to the term used as benchmark; the temporal relationships among the 12 disease search volumes and their temporal sequences similarity by dynamic time warping analyses was used to predict the comorbid diseases. Among the 12 lifestyle diseases, the study provides an indirect evidence in the existence of severe seasonal comorbidity among hypertension, obesity, asthma and fibrosis diseases, which is widespread in the world population. Thus, the present study has successfully addressed this issue by predicting the SCLD, and indirectly verified them among the world population using Google Search Trend. Furthermore, based on the SCLD seasonal trend, the study also classified them as severe, moderate, and mild. Interestingly, seasonal trends of the severe seasonal comorbid diseases displayed an inverse pattern between USA (Northern hemisphere) and New Zealand (Southern hemisphere). Further, knowledge in the so called “seasonal sensitive populations” physiological response to seasonal triggers such as winter, summer, spring, and autumn become crucial to modulate disease incidence, disease course, or clinical prevention.

季节变化与人类生理状态改变是诸多疾病发生发展的重要影响因素。然而，由于诸多直接影响疾病发生的环境与生活方式因素并存，精准量化季节对这类疾病的真实影响颇具难度。尽管如此，全球多地已开展多项临床研究，明确证实特定人群对季节变化高度敏感，其生理适应不良可能引发多种病症。因此，探究在人群中广泛分布的季节敏感性疾病具有重要意义。

为筛选相关病症，本研究提出假说：与高海拔（high altitude, HA）相关的疾病与病症，属于季节性生理变化的强变异类型。究其原因，高海拔是唯一能促使人类演化出高效生理适应机制——习服（acclimatization）的地理环境。为验证该假说，研究人员依托PubMed数据库收集高海拔相关症状与病症信息，并构建基于疾病本体论（Disease Ontology）的语义相似性网络（DSN）与疾病-药物网络，以筛选高海拔基准病症。通过对语义相似性网络开展多维度群落结构分析，本研究挖掘出与高海拔高度相关或可能共病的疾病，并将筛选出的12种生活方式相关疾病定义为‘季节性敏感共病生活方式疾病（seasonal sensitive comorbid lifestyle diseases, SCLD）’。

为验证这12种疾病是否存在季节性规律，研究针对2004至2016年全球谷歌搜索数据开展时序分析。鉴于搜索趋势对基准术语选取较为敏感，本研究通过动态时间规整（dynamic time warping）分析12种疾病搜索量的时序关联与序列相似性，以预测共病关系。在12种生活方式相关疾病中，本研究间接证实高血压、肥胖症、哮喘与纤维化疾病之间存在广泛的全球性严重季节性共病现象。综上，本研究成功预测出SCLD，并通过谷歌搜索趋势在全球人群中完成间接验证。此外，基于SCLD的季节趋势特征，研究将其划分为重度、中度与轻度三类。

值得注意的是，重度季节性共病疾病的季节趋势在北半球的美国与南半球的新西兰之间呈现完全相反的模式。此外，针对所谓‘季节敏感人群’对冬季、夏季、春季、秋季等季节触发因素的生理响应开展研究，对调控疾病发病率、疾病进程或临床预防均具有重要价值。