Microbiome analysis revealing microbial interactions and secondary bacterial infections in COVID-19 patients comorbidly affected by Type 2 diabetes.
Hassan M Al-EmranShaminur RahmanMd Shazid HasanRubayet Ul AlamOvinu Kibria IslamAjwad AnwarMd Iqbal K JahidMohammad Anwar HossainPublished in: Journal of medical virology (2022)
The mortality of coronavirus disease 2019 (COVID-19) disease is very high among the elderly or individuals having comorbidities such as obesity, cardiovascular diseases, lung infections, hypertension, and/or diabetes. Our study characterizes the metagenomic features in severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)-infected patients with or without type 2 diabetes, to identify the microbial interactions associated with its fatal consequences.This study compared the baseline nasopharyngeal microbiome of SARS-CoV-2-infected diabetic and nondiabetic patients with controls adjusted for age and gender. The metagenomics based on next-generation sequencing was performed using Ion GeneStudio S5 Series and the data were analyzed by the Vegan-package in R. All three groups possessed significant bacterial diversity and dissimilarity indexes (p < 0.05). Spearman's correlation coefficient network analysis illustrated 183 significant positive correlations and 13 negative correlations of pathogenic bacteria (r = 0.6-1.0, p < 0.05), and 109 positive correlations between normal flora and probiotic bacteria (r > 0.6, p < 0.05). The SARS-CoV-2 diabetic group exhibited a significant increase in pathogens and secondary infection-causing bacteria (p < 0.05) with a simultaneous decrease of normal flora (p < 0.05). The dysbiosis of the bacterial community might be linked with severe consequences of COVID-19-infected diabetic patients, although a few probiotic strains inhibited numerous pathogens in the same pathological niches. This study suggested that the promotion of normal flora and probiotics through dietary supplementation and excessive inflammation reduction by preventing secondary infections might lead to a better outcome for those comorbid patients.
Keyphrases
- sars cov
- respiratory syndrome coronavirus
- type diabetes
- coronavirus disease
- cardiovascular disease
- network analysis
- insulin resistance
- glycemic control
- blood pressure
- magnetic resonance imaging
- metabolic syndrome
- risk factors
- machine learning
- newly diagnosed
- mental health
- magnetic resonance
- skeletal muscle
- gene expression
- prognostic factors
- copy number
- weight loss
- ejection fraction
- circulating tumor
- community dwelling
- diffusion weighted imaging