Google trend analysis of the Indian population reveals a panel of seasonally sensitive comorbid symptoms with implications for monitoring the seasonally sensitive human population

Abstract

Seasonal variations in the environment induce observable changes in the human physiological system and manifest as various clinical symptoms in a specific human population. Our earlier studies predicted four global severe seasonal sensitive comorbid lifestyle diseases (SCLDs), namely, asthma, obesity, hypertension, and fibrosis. Our studies further indicated that the SCLD category of the human population may be maladapted or unacclimatized to seasonal changes. The current study aimed to explore the major seasonal symptoms associated with SCLD and evaluate their seasonal linkages via Google Trends (GT). We used the Human Disease Symptom Network (HSDN) to dissect common symptoms of SCLD. We then exploited medical databases and medical literature resources in consultation with medical practitioners to narrow down the clinical symptoms associated with four SCLDs, namely, pulmonary hypertension, pulmonary fibrosis, asthma, and obesity. Our study revealed a strong association of 12 clinical symptoms with SCLD. Each clinical symptom was further subjected to GT analysis to address its seasonal linkage. The GT search was carried out in the Indian population for the period from January 2015-December 2019. In the GT analysis, 11 clinical symptoms were strongly associated with Indian seasonal changes, with the exception of hypergammaglobulinemia, due to the lack of GT data in the Indian population. These 11 symptoms also presented sudden increases or decreases in search volume during the two major Indian seasonal transition months, namely, March and November. Moreover, in addition to SCLD, several seasonally associated clinical disorders share most of these 12 symptoms. In this regard, we named these 12 symptoms the "seasonal sensitive comorbid symptoms (SSC)" of the human population. Further clinical studies are needed to verify the utility of these symptoms in screening seasonally maladapted human populations. We also warrant that clinicians and researcher be well aware of the limitations and pitfalls of GT before correlating the clinical outcome of SSC symptoms with GT.

Keywords: Asthma; Obesity; Pulmonary fibrosis; Pulmonary hypertension; Seasonality; Seasonally sensitive population.

Fig. 1

SCLD namely Asthma, Pulmonary Fibrosis, and Pulmonary Hypertension were encircled in red color. The symptoms of each disease extracted from HSDN with TF-IDF scores > 2.0 were shown in different colors. The symptoms shared among all three diseases are shown in yellow color. The symptoms shared between Asthma and Pulmonary fibrosis were shown in green color. The symptoms shared between Asthma and Pulmonary hypertension were shown in indigo color. The symptoms associated with only one disease were represented in cyan color (Asthma), turquoise color (Pulmonary Fibrosis), and magenta (Pulmonary Hypertension)

Fig. 2

The sub-network of common symptoms of SCLD with TF-IDF score > 2.0 were shown. The disease nodes were represented in purple, and 7 common symptom nodes were shown in pink, encrypted with the corresponding symptom names

Fig. 3

The Venn diagram of SCLD symptoms collected from literature resources was shown. The diseases were depicted in red color and ICD10 of symptoms were in blue. Note that the symptoms namely fatigue and shortness of breath were shared among three diseases, and the remaining 5 symptoms were shared among two diseases

Fig. 4

The Indian monthly average RSV of 9 co-existing symptoms of SCLD for the Years 2004 to 2009 were shown in different colors. Note that out of 9 symptoms with RSV, the four symptoms namely fatigue, obesity, edema, and sweating showed their average RSV > 43 with obesity as the benchmark

Fig. 5

Box plot of seasonal average levels of sweating and snoring is shown. The horizontal solid lines indicate the mean values and range whereas the top and bottom of the box indicate the 75th and 25th percentiles. These two symptoms defined two seasons summer and winter with equal 6-month lengths that spanned a single calendar year such as two semesters (e.g. December–July vs. July–December). Both symptoms showed almost opposite minimum and maximum values in the entire year. Note the apparent prominent changes in the RSV during November and March peaks before the RSV could reach either minimum or maximum values

Fig. 6

Box plots of seasonal average levels of severe headache, fatigue, dry cough, shortness of breath, and edema are shown. All these five symptoms are well defined in four Indian seasons spanned in a single calendar year (a) winter (December to February), summer (March to May), monsoon or rainy season (June to September), and a post-monsoon period (October and November). Note the apparent prominent changes in the RSV during November and March peaks before the RSV could reach either minimum or maximum values

Fig. 7

Box plots of seasonal average levels of high fever, obesity, cyanosis, and sleep deprivation are shown. All these four symptoms moderately defined four Indian seasons spanned in a single calendar year (a) winter (December to February), summer (March to May), monsoon or rainy season (June to September), and a post-monsoon period (October and November). Note the apparent changes in RSV during November and March peaks before the RSV could reach either minimum or maximum values

Fig. 8

The seasonal decomposition of seasonal symptoms of sweating and snoring for the monthly averaged RSV from 2015 to 2019 with obesity as the benchmark symptom. Note the opposite seasonal trends in the sweating and snoring

Fig. 9

The seasonal decomposition of quarterly seasonal symptoms of dry cough, high fever, fatigue, shortness of breath, and severe headache for the monthly averaged RSV from 2015 to 2019 with obesity as the benchmark symptom. Note the repeating distinct seasonal patterns in all the symptoms

Fig. 10

The seasonal decomposition of quarterly seasonal symptoms of cyanosis and sleep deprivation for the monthly averaged RSV from 2015 to 2019 without obesity as the benchmark symptom. The seasonal decomposition of quarterly seasonal symptoms of obesity and edema with the benchmark is also shown. Note the repeating distinct seasonal patterns in all the symptoms