Cardiovascular diseases display etiological and seasonal trend in human population: Evidence from seasonal cardiovascular comorbid diseases (SCCD) index.

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.

Google trend analysis of climatic zone based Indian severe seasonal sensitive population.

BACKGROUND: Our earlier Google Trend (GT) Analytics study reported that the worldwide human population severely subject to four seasonal (sensitive) comorbid lifestyle diseases (SCLD) such as asthma, obesity, hypertension and fibrosis. The human population subject to seasonal variability in these four diseases activity referred as "severe seasonal sensitive population". In India, the estimated burden of these four seasonal diseases is more than 350 million as on the year 2018. It is a growing crisis for India with a projected disease burden of 500 million in the year 2025. This study was aimed to decipher the genuine SCLD seasonal trends in the entire Indian population using GT and validate these trends in Indian climatic zones. METHODS: GT is used to study the temporal trends in web search using weekly Relative Search Volume (RSV) for the period 2004 to 2017. The relative search volume (RSV) of the four-severe seasonal comorbid diseases namely Asthma, Hypertension, Obesity and Fibrosis were collected with and without obesity as the reference. The RSV were collected using the GT selection options as (i) Whole India (ii) Jammu and Kashmir (Cold zone) (iii) Rajasthan (Hot and Dry zone) (iii) West Bengal (Hot and Humid zone) and (iv) Uttar Pradesh state (Composite zone). The time series analysis was carried out to find seasonal patterns, comorbidity, trends and periodicity in the entire India and four of its states (zones). RESULTS: Our analysis of entire India (2004-2017) revealed high significant seasonal patterns and comorbidity in all the four diseases of SCLD. The positive tau values indicated strong positive seasonal trends in the SCLD throughout the period (Table). The auto correlation analysis revealed that these diseases were subjected to 3, 4 and 6 months period seasonal variations. Similar seasonal patterns and trends were also observed in all the four Indian temperature zones. Overall study indicated that SCLD seasonal search patterns and trends are highly conserved in India even in drastic Indian climatic zones. CONCLUSIONS: The clinical outcome arise out of these observations could be of immense significance in handling the major chronic life style diseases asthma, hypertension, obesity and fibrosis. The possible strong comorbid relationship among asthma, hypertension, obesity and fibrosis may be useful to segregate Indian seasonal sensitive population. In disease activity-based chronotherapy, the search interest of segment of the population with access to Internet may be used as an indicator for public health sectors in the early detection of SCLD from a specific country or a region. As this disease population could be highly subject to the adverse effect of seasons in addition to life style and other environmental factors. Our study necessitates that these Indian populations need special attention from the Indian health care sectors.

A Machine Learning Model for the Prediction of COVID-19 Severity Using RNA-Seq, Clinical, and Co-Morbidity Data.

The premise for this study emanated from the need to understand SARS-CoV-2 infections at the molecular level and to develop predictive tools for managing COVID-19 severity. With the varied clinical outcomes observed among infected individuals, creating a reliable machine learning (ML) model for predicting the severity of COVID-19 became paramount. Despite the availability of large-scale genomic and clinical data, previous studies have not effectively utilized multi-modality data for disease severity prediction using data-driven approaches. Our primary goal is to predict COVID-19 severity using a machine-learning model trained on a combination of patients' gene expression, clinical features, and co-morbidity data. Employing various ML algorithms, including Logistic Regression (LR), XGBoost (XG), Naïve Bayes (NB), and Support Vector Machine (SVM), alongside feature selection methods, we sought to identify the best-performing model for disease severity prediction. The results highlighted XG as the superior classifier, with 95% accuracy and a 0.99 AUC (Area Under the Curve), for distinguishing severity groups. Additionally, the SHAP analysis revealed vital features contributing to prediction, including several genes such as COX14, LAMB2, DOLK, SDCBP2, RHBDL1, and IER3-AS1. Notably, two clinical features, the absolute neutrophil count and Viremia Categories, emerged as top contributors. Integrating multiple data modalities has significantly improved the accuracy of disease severity prediction compared to using any single modality. The identified features could serve as biomarkers for COVID-19 prognosis and patient care, allowing clinicians to optimize treatment strategies and refine clinical decision-making processes for enhanced patient outcomes.

iCluF: an unsupervised iterative cluster-fusion method for patient stratification using multiomics data.

Motivation: Patient stratification is crucial for the effective treatment or management of heterogeneous diseases, including cancers. Multiomic technologies facilitate molecular characterization of human diseases; however, the complexity of data warrants the need for the development of robust data integration tools for patient stratification using machine-learning approaches. Results: iCluF iteratively integrates three types of multiomic data (mRNA, miRNA, and DNA methylation) using pairwise patient similarity matrices built from each omic data. The intermediate omic-specific neighborhood matrices implement iterative matrix fusion and message passing among the similarity matrices to derive a final integrated matrix representing all the omics profiles of a patient, which is used to further cluster patients into subtypes. iCluF outperforms other methods with significant differences in the survival profiles of 8581 patients belonging to 30 different cancers in TCGA. iCluF also predicted the four intrinsic subtypes of Breast Invasive Carcinomas with adjusted rand index and Fowlkes-Mallows scores of 0.72 and 0.83, respectively. The Gini importance score showed that methylation features were the primary decisive players, followed by mRNA and miRNA to identify disease subtypes. iCluF can be applied to stratify patients with any disease containing multiomic datasets. Availability and implementation: Source code and datasets are available at https://github.com/GudaLab/iCluF_core.

Identification of VEGFA-centric temporal hypoxia-responsive dynamic cardiopulmonary network biomarkers.

AIMS: Hypoxia, a pathophysiological condition, is profound in several cardiopulmonary diseases (CPD). Every individual's lethality to a hypoxia state differs in terms of hypoxia exposure time, dosage units and dependent on the individual's genetic makeup. Most of the proposed markers for CPD were generally aim to distinguish disease samples from normal samples. Although, as per the 2018 GOLD guidelines, clinically useful biomarkers for several cardio pulmonary disease patients in stable condition have yet to be identified. We attempt to address these key issues through the identification of Dynamic Network Biomarkers (DNB) to detect hypoxia induced early warning signals of CPD before the catastrophic deterioration. MATERIALS AND METHODS: The human microvascular endothelial tissues microarray datasets (GSE11341) of lung and cardiac expose to hypoxia (1% O2) for 3, 24 and 48 h were retrieved from the public repository. The time dependent differentially expressed genes were subjected to tissue specificity and promoter analysis to filtrate the noise levels in the networks and to dissect the tissue specific hypoxia induced genes. These filtered out genes were used to construct the dynamic segmentation networks. The hypoxia induced dynamic differentially expressed genes were validated in the lung and heart tissues of male rats. These rats were exposed to hypobaric hypoxia (simulated altitude of 25,000 or PO2 - 282 mm of Hg) progressively for 3, 24 and 48 h. KEY FINDINGS: To identify the temporal key genes regulated in hypoxia, we ranked the dominant genes based on their consolidated topological features from tissue specific networks, time dependent networks and dynamic networks. Overall topological ranking described VEGFA as a single node dynamic hub and strongly communicated with tissue specific genes to carry forward their tissue specific information. We named this type of VEGFAcentric dynamic networks as "V-DNBs". As a proof of principle, our methodology helped us to identify the V-DNBs specific for lung and cardiac tissues namely V-DNBL and V-DNBC respectively. SIGNIFICANCE: Our experimental studies identified VEGFA, SLC2A3, ADM and ENO2 as the minimum and sufficient candidates of V-DNBL. The dynamic expression patterns could be readily exploited to capture the pre disease state of hypoxia induced pulmonary vascular remodelling. Whereas in V-DNBC the minimum and sufficient candidates are VEGFA, SCL2A3, ADM, NDRG1, ENO2 and BHLHE40. The time dependent single node expansion indicates V-DNBC could also be the pre disease state pathological hallmark for hypoxia-associated cardiovascular remodelling. The network cross-talk and expression pattern between V-DNBL and V-DNBC are completely distinct. On the other hand, the great clinical advantage of V-DNBs for pre disease predictions, a set of samples during the healthy condition should suffice. Future clinical studies might further shed light on the predictive power of V-DNBs as prognostic and diagnostic biomarkers for CPD.

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.