ABSTRACT
IntroductionCOVID-19 pandemic has caused havoc worldwide, and different comorbidities have been seen to exacerbate the condition. Obesity is one of the leading comorbidities, which is associated with many other diseases. In this paper, we present a systematic review and meta-analysis estimating the effects of overweight and obesity on COVID-19 disease severity. MethodologyTwo electronic databases (Medline and Cochrane library) and one grey literature database (Grey Literature Report) were searched using the following keywords: overweight, obesity, body mass index, respiratory disease, coronavirus, COVID-19. The risks of bias of the selected studies were assessed by using the Navigation Guide method for human data. Both random and fixed effect meta-analysis were determined using Review Manager (RevMan) software version 5.4. ResultsAfter initial screening, 12 studies (7 cohort studies, four case-control studies, and one cross-sectional study) were fulfilled the eligibility criteria, comprising a total of 405359 patients and included in the systematic review. The pooled risk of disease severity was 1.31 times higher based on both fixed and random effect model among those overweight patients, I2 0% and 2.09 and 2.41 times higher based on fixed and random effect respectively among obese patients, I2 42% compared to healthy individuals. ConclusionOverweight and obesity are common risk factors for disease severity of COVID-19 patients. However, further assessment of metabolic parameters included BMI, waist-hip ratio, and insulin levels, are required to estimate the risk factors of COVID-19 patients and understanding the mechanism between COVID-19 and body mass index.
ABSTRACT
BackgroundThe global pandemic of COVID-19 is posing the biggest threat to humanity through its ubiquitous effect of unfathomable magnitude. It has been responsible for over four hundred thousand death worldwide to date. There has been evidence that various comorbidities have a higher risk associated with case fatality. Although COVID-19 is a viral disease, there might be an association between different comorbidities and the occurrence of the disease. MethodSociodemographic and medical history data on different comorbidities such as asthma, diabetes, liver disease, lung disease, heart disease, kidney disease, hypertension, and obesity were collected by a web-based self-reported survey between 25th March 2020 to 4th June 2020 by the Nexoid United Kingdom. Univariate and multivariate logistic regression analyses were done using these risk factors as independent variables. ResultA total of 780,961 participants from 183 different countries and territories participated in this study. Among them, 1516 participants were diagnosed with COVID-19 prior to this study. A significant risk association was observed for age above 60 years, female gender as well as different pre-existing disease conditions such as diabetes, kidney disease, liver disease, and heart diseases. Asthma and diabetes were the major dominant comorbidities among patients, and patients with existing diabetes were 1.464 (AOR: 1.464; 95% CI: 1.228-1.744), more likely to develop the disease than others who did not diagnose as diseased. ConclusionOlder adults, female as well as people with comorbidities such as diabetes mellitus, heart disease, kidney disease, and liver disease, are the most vulnerable population for COVID-19. However, further studies should be carried out to explain the pathway of these risk associations.
ABSTRACT
Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is the novel coronavirus responsible for the ongoing pandemic of coronavirus disease (COVID-19). No sustainable treatment option is available so far to tackle such a public health threat. Therefore, designing a suitable vaccine to overcome this hurdle asks for immediate attention. In this study, we targeted for a design of multi-epitope based vaccine using immunoinformatics tools. We considered the structural proteins S, E and, M of SARS-CoV-2, since they facilitate the infection of the virus into host cell and using different bioinformatics tools and servers, we predicted multiple B-cell and T-cell epitopes having potential for the required vaccine design. Phylogenetic analysis provided insight on ancestral molecular changes and molecular evolutionary relationship of S, E, and M proteins. Based on the antigenicity and surface accessibility of these proteins, eight epitopes were selected by various B cell and T cell epitope prediction tools. Molecular docking was executed to interpret the binding interactions of these epitopes and three potential epitopes WTAGAAAYY, YVYSRVKNL, and GTITVEELK were selected for their noticeable higher binding affinity scores -9.1, -7.4, and -7.0 kcal/mol, respectively. Targeted epitopes had 91.09% population coverage worldwide. In summary, we identified three epitopes having the most significant properties of designing the peptide-based vaccine against SARS-CoV-2.
