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, asthm...
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BMC
2024-12-01
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| Series: | Population Health Metrics |
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| Online Access: | https://doi.org/10.1186/s12963-024-00349-7 |
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| author | Urmila Gahlot Yogendra Kumar Sharma Jaichand Patel Sugadev Ragumani |
| author_facet | Urmila Gahlot Yogendra Kumar Sharma Jaichand Patel Sugadev Ragumani |
| author_sort | Urmila Gahlot |
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| description | 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. |
| format | Article |
| id | doaj-art-c111c035b1314d1ebce3a692dd030631 |
| institution | Kabale University |
| issn | 1478-7954 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
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| series | Population Health Metrics |
| spelling | doaj-art-c111c035b1314d1ebce3a692dd0306312025-01-05T12:42:53ZengBMCPopulation Health Metrics1478-79542024-12-0122111310.1186/s12963-024-00349-7Google trend analysis of the Indian population reveals a panel of seasonally sensitive comorbid symptoms with implications for monitoring the seasonally sensitive human populationUrmila Gahlot0Yogendra Kumar Sharma1Jaichand Patel2Sugadev Ragumani3Bioinformatics Group, Defense Institute of Physiology and Allied Sciences, Defense Research and Development OrganizationBioinformatics Group, Defense Institute of Physiology and Allied Sciences, Defense Research and Development OrganizationBioinformatics Group, Defense Institute of Physiology and Allied Sciences, Defense Research and Development OrganizationBioinformatics Group, Defense Institute of Physiology and Allied Sciences, Defense Research and Development OrganizationAbstract 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.https://doi.org/10.1186/s12963-024-00349-7AsthmaObesityPulmonary hypertensionPulmonary fibrosisSeasonalitySeasonally sensitive population |
| spellingShingle | Urmila Gahlot Yogendra Kumar Sharma Jaichand Patel Sugadev Ragumani Google trend analysis of the Indian population reveals a panel of seasonally sensitive comorbid symptoms with implications for monitoring the seasonally sensitive human population Population Health Metrics Asthma Obesity Pulmonary hypertension Pulmonary fibrosis Seasonality Seasonally sensitive population |
| title | Google trend analysis of the Indian population reveals a panel of seasonally sensitive comorbid symptoms with implications for monitoring the seasonally sensitive human population |
| title_full | Google trend analysis of the Indian population reveals a panel of seasonally sensitive comorbid symptoms with implications for monitoring the seasonally sensitive human population |
| title_fullStr | Google trend analysis of the Indian population reveals a panel of seasonally sensitive comorbid symptoms with implications for monitoring the seasonally sensitive human population |
| title_full_unstemmed | Google trend analysis of the Indian population reveals a panel of seasonally sensitive comorbid symptoms with implications for monitoring the seasonally sensitive human population |
| title_short | Google trend analysis of the Indian population reveals a panel of seasonally sensitive comorbid symptoms with implications for monitoring the seasonally sensitive human population |
| title_sort | google trend analysis of the indian population reveals a panel of seasonally sensitive comorbid symptoms with implications for monitoring the seasonally sensitive human population |
| topic | Asthma Obesity Pulmonary hypertension Pulmonary fibrosis Seasonality Seasonally sensitive population |
| url | https://doi.org/10.1186/s12963-024-00349-7 |
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