In a new study, researchers have recently applied an advanced statistical approach to analyze risk factors that may be causally related to COVID-19 infection.
Results suggested that diabetes may be a risk factor leading to increased susceptibility to or severity of COVID-19 infection through changes in ACE2 expression, which is a key receptor for the virus.
The research was conducted by a team from The Chinese University of Hong Kong (CU Medicine).
The COVID-19 pandemic has affected more than a hundred countries or regions worldwide, and more than 11 million confirmed cases have been reported.
It is urgent to seek solutions to control the spread of the disease to susceptible groups and to identify effective treatments.
A better understanding of its pathophysiology is also desperately needed.
A substantial proportion of COVID-19 death cases in Hong Kong suffered from diabetes.
In the study, the team applied an advanced statistical approach to analyze risk factors that may be causally related to the disease.
The team made use of “big data” from genome-wide association studies to explore diseases and blood proteins causally linked to altered ACE2 expression in the lung.
There is sound evidence that ACE2 is a key receptor for COVID-19, and high expression of ACE2 may increase susceptibility to infection.
Through a screen of over 500 diseases or traits, the most consistent finding was tentative evidence of an association between diabetes-related traits and increased ACE2 expression.
Significant and positive associations with ACE2 expression were observed across multiple diabetes datasets and analytic methods for type 1 and 2 diabetes as well as related traits including early start of insulin.
The results suggest that diabetes may be a risk factor leading to increased susceptibility to or severity of COVID-19 infection through changes in ACE2 expression.
As an exploratory analysis, blood proteins linked to altered ACE2 expression were also found and examined by the team.
This may help elucidate potential molecular mechanisms, and serve as potential biomarkers and guide drug development or repurposing in the future.
The team says there is great potential for using genomic big data to uncover risk factors and treatments for COVID-19.
One important aspect of this study is that statistical analysis is better at delineating causal relationships than observational studies.
They hope the current work will shed light on new research strategies and lead to more confirmatory studies in this area in the future.
One author of the study is Dr. Hon Cheong SO, Assistant Professor of the School of Biomedical Sciences at CU Medicine.
The study is published in Diabetes Care.
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