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Resumo

Seasonal changes in the human cardiovascular system are known to play an important role in the onset of many diseases. Confounding variables include behavioral and environmental factors; failing to address such variables makes measuring the true temporal impact of these diseases difficult. On the other hand, numerous clinical studies imply that only specific groups of people are more seasonal sensitive and that their maladaptation might contribute to various illnesses. As a result, it is critical to evaluate the etiological and seasonal sensitive patterns of cardiovascular diseases (CVD), which impact the majority of the human population. The hypothesis for this study formulated that cardiovascular and associated illnesses had substantial connections with seasonal and etiological variations. Thus in the present study, 4519 systematic screen‐eligible studies were analyzed using data mining to uncover 852 disease association relationships between cardiovascular and associated disorders. A disease ontology‐based semantic similarity network (DSN) analysis was performed to narrow down the identified CVDs. Further, topological analysis was used to predict the seven CVDs, including myocardial infarction (MI), in three clusters. Following that, Mann–Kendall and Cox–Stuart analyses were used to investigate the seasonal sensitivity and temporal relationship of these seven CVDs. Finally, temporal relationships were confirmed using LOESS and TBATS, as well as seasonal breakdown utilizing autocorrelation and fast Fourier transform results. The study provides indirect evidence of a severe etiological association among the three cardiovascular diseases, including MI, atrial fibrillation, and atherosclerosis, which are winter season sensitive in most of the world population. Hypertension has two seasonal falls and peaks due to its seasonal nature, that is, summer and winter hypertension. While, heart failure was also identified, with minor temporal trends. Hence, all five diseases could be classified as seasonal cardiovascular comorbid diseases (SCCD). Furthermore, these diseases could be studied for potential common risk factors such as biochemical, genetic, and physiological factors.

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