The COVID-19 outbreak is far from over, with a rapid increase in cases in the US, and in many South American countries, as well as in Russia and India. The speed and ease of transmission have left many observers dismayed, and public health experts are working strenuously to identify the risk factors for infection.
Now, a new study by researchers at the University of Ulm, Germany, which is published on the preprint server medRxiv* in July 2020, focuses on how blood group distribution is related to the mapped dynamics of the epidemic as shown by its geographical distribution.
Relying on the infection map put up by John Hopkins University (JHU), they point out a clear difference in the case incidence by location. The initial outbreak in China was followed by its rapid spread to Europe, and almost immediately to New York City. The virus then disseminated to Latin America and the East Mediterranean regions.
Risk Factors: The Importance of Blood Type
In trying to tease out the risk factors, the importance of blood types should not be overlooked, argue the researchers. These have been associated with many infectious diseases like HIV and malaria, and influenza, which itself has caused earlier pandemics.
Prior studies report that among hospitalized COVID-19 cases, the blood type A was significantly over-represented relative to blood type B. Moreover, the blood type has been reported to contribute to the clinical severity and outcome of the infection as well.
The current study begins where the earlier studies end. The researchers point out that earlier studies were on people in a small locality or in limited numbers so that the data comes from a single region rather than from worldwide populations. Thus, there has not been any attempt to study the global dynamics of this infection.
The Study: Population Risk by Blood Type
The researchers performed the current study using big data to find the association, if any, between the pandemic and ABO blood types. The data came from the World Health Organization (WHO).
The dynamics of any infectious disease are essential in determining the spread. They are defined by a number of characteristics, including infection case growth rate (ICGR), infection case doubling time (IC-dt), reproductive number (RN), death case growth rate (DCGR), and death case doubling time (DC-dt).
In the current pandemic, the total number of infections is yet to be known as it is still growing actively. Hence, the researchers looked at the half-way point of the exponential phase of the infection, using the JHU epidemic curves, to assess both the number of infections and the number of deaths for each country included in the study.
The study included six geographic regions, namely, Africa, America, East Mediterranean, Europe, South East Asia, and the Western Pacific. Six countries were randomly included per region. Those with very few cases were subsequently excluded, leaving 34 countries with a total population of about 5.3 billion.
The blood group distribution of each country was then determined from earlier research. Based on the possible relevance of blood type A, the researchers found the mean proportion of blood type A to be about 30% in all the countries in the study. They then divided the countries into two groups: those with blood type A in over 30% of the population, and those with less than 30% blood type A.
Study: Association between epidemic dynamics of COVID-19 infection and ABO blood group types. Image Credit: Sura Nualpradid/ Shutterstock
This news article was a review of a preliminary scientific report that had not undergone peer-review at the time of publication. Since its initial publication, the scientific report has now been peer reviewed and accepted for publication in a Scientific Journal. Links to the preliminary and peer-reviewed reports are available in the Sources section at the bottom of this article. View Sources
Blood Type A and Infection/Death Rates
The study found that ICGR was higher with blood type A but lower in blood type B, with no correlation with blood types O or AB. Similar correlations existed with the DCGR. As the exponential phase began, the number of infections was similar for populations with higher or lower proportions of blood type A. However, at Dpp1/2 the number of infections was markedly higher in the former populations than the latter.
In the first 15 days of exponential growth, the number of infections grew much faster in the higher blood type A group, while the doubling time shortened as the proportion of type A blood group increased. The reproductive number showed no difference between the two groups, as expected.
The changes in the death number, as shown by DCGR, indicated that the number of deaths also grew much faster in the group with a more significant proportion of type A blood types, and the doubling time for the number of deaths was shorter in this group, accordingly.
Clinical Implications
The researchers point out that with the current pandemic, four of the established methods to stop an epidemic are unavailable: stopping the origin of the virus, since it is still unknown; effective treatment, since none is yet available; and improving herd immunity, in the absence of a vaccine. Thus, the only way out is the fourth, that is, breaking the transmission chain.
Lockdowns and less severe restrictions on individual mobility have been part of the attempt to break the chain. The adverse effects of lockdown are, however, so significant as to make its duration a matter of critical interest. This requires knowing how the dynamics of an outbreak relate to the health risk of the population.
Big data was used here to overcome the limitations caused by inaccurate and limited testing. The researchers attempted to build on earlier studies that showed a preponderance of type A among COVID-19 cases. The results confirm an association between the number of cases and deaths due to COVID-19, and the proportion of type A blood in the whole population, and a negative linkage between cases/deaths and type B blood.
The findings show that except for the number of cases at the beginning of the exponential phase and the reproductive number, all measured parameters were significantly higher in the populations with a higher proportion of type A blood.
The researchers sum up: “People with blood group A are therefore more susceptible to COCID-19 infection, and their case fatality rate is higher.”
It may be that the attachment of the SARS-CoV-2 S protein to the host cell receptor, the angiotensin-converting enzyme 2 (ACE2), is affected by the blood type, perhaps because type A people express the receptor at higher levels. More research will be needed to understand how the blood type contributes to infection number and severity.
Unpublished research by the same team also shows that the life expectancy of the population and the healthcare expenditure is also linked to the number of cases, which confirms earlier research showing a higher vulnerability to infection among older people, but not with healthcare expenditure.
Countries with lower proportions of individuals with type A blood have a lower income and healthcare resources, overall, which could drop still lower in the event of a lockdown.
The study concludes, therefore, “It might be reasonable for these countries to make slower or later strict measures owing to the lower epidemic dynamics of COVID-19 infection, whereas for those countries where the people with a higher proportion of blood type A the strict measure to combat the COVID -19 infection ought to be more active and earlier.”
This news article was a review of a preliminary scientific report that had not undergone peer-review at the time of publication. Since its initial publication, the scientific report has now been peer reviewed and accepted for publication in a Scientific Journal. Links to the preliminary and peer-reviewed reports are available in the Sources section at the bottom of this article. View Sources
Article Revisions
- Mar 23 2023 - The preprint preliminary research paper that this article was based upon was accepted for publication in a peer-reviewed Scientific Journal. This article was edited accordingly to include a link to the final peer-reviewed paper, now shown in the sources section.
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