Association between ABO blood groups and risk of COVID-19 infection: An umbrella review

INTRODUCTION

SARS-CoV-2, a novel coronavirus that causes the new infectious Coronavirus Disease-2019 (COVID-19), is rapidly spreading throughout the world and was recently declared a pandemic by the World Health Organization (WHO).^[1-3] As of 5:22 p.m. CEST on August 20, 2021, the WHO had confirmed 209,876,613 cases of COVID-19 worldwide, including 4,400,284 deaths.^[4]

Recently, several studies discovered a link between the ABO blood group and COVID-19 morbidity and mortality.^[2] In addition, epidemiology has identified risk factors for SARS-CoV-2 (COVID-19) infection, including age, gender, chronic disease, and smoking.^[2,5-7] The findings, however, are preliminary and controversial. Researchers’ findings regarding the highest risk of infection, specifically the association between ABO blood groups and the risk of COVID-19 infection, are varied. Most researchers report that individuals with blood type A face the most significant risk of infection with COVID-19, while others report that individuals with blood type B face the greatest risk of infection. Although some researchers found no correlation between blood types and COVID-19 severity or mortality, the majority of studies discovered that blood types A and AB were associated with an increased risk of severe illness or death. In contrast, blood type O was associated with a protective effect against death or severe outcomes.^[8] To this end, the current umbrella review was conducted using a narrative approach to synthesize the systematic reviews and meta-analyses regarding the association between the ABO blood group and COVID-19 infection.

MATERIAL AND METHODS

This review was conducted per the guidelines for Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA).^[9]

RESULTS

Database and search strategy

The Google Scholar, Google, and Cochrane Databases of Systematic Reviews literature searches returned 140 records. After removing duplicates, the authors screened 115 titles and abstracts, excluding 106 records that did not meet the inclusion criteria. Finally, the authors conducted a thorough review of the remaining studies’ full texts. Four studies remained after applying the inclusion and exclusion criteria and were included in the present or this umbrella review. In addition, the reference lists of the four studies included were screened for pertinent articles. Figure 1 illustrates the PRISMA flow diagram^[11,12] for the literature search.

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Figure 1:

The PRISMA flow diagram for the literature search.

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DISCUSSION

To the best of the authors’ knowledge, this is the first umbrella review examining the relationship between ABO blood groups and susceptibility to COVID-19 infection.

All of the studies included in this umbrella review (100%) suggested that blood group O may be associated with a reduced risk of susceptibility to COVID-19 infection. However, only 75% of the studies included in this study suggested that blood group A may be associated with an increased risk of COVID-19 infection. In other words, all of the studies included in this umbrella review concluded that blood type O might act as a protective factor against COVID-19 infection, while the majority concluded that blood type A might act as a risk factor.

This finding is consistent with some previous research on the association between ABO blood groups and SARSCoV-2 (COVID-19) infection.^[8,13-16] This finding, however, contradicts recent meta-analytic studies on the association between ABO blood groups and SARS-CoV-2 (COVID-19) infection.

In addition, this umbrella review revealed that the findings from the included studies on the association between ABO blood groups and COVID-19 mortality outcomes were more inconsistent and mixed.

Moreover, the current umbrella review uncovered significant heterogeneity across all included studies, which could account for inconsistent, discordant, or mixed findings. Between-study differences, including methodological differences, such as differences in the treatment, the treated population, the study design, the study setting, the variations in ABO blood group frequencies between populations with varying geographical origins and ancestries, or the data analysis method, may contribute to such heterogeneity. In addition, methodological issues such as flaws in primary study design and participant differences^[3,15,17-20] such as sex, age, ethnic origin, lifestyle, beliefs, smoking, and comorbidity with chronic diseases may contribute to the varied results.

For instance, when only data on critically ill patients are analyzed, critical risk factors such as sex, age, obesity, cardiovascular, and underlying metabolic diseases are overlooked. The ABO polymorphism has been linked to several of these COVID-19 risk factors.^[15] The current umbrella review revealed that most studies included in this (75%) reported that the majority of COVID-19 cases occurred in hospitals.

Similarly, the study’s design was criticized for relying heavily on blood donors as controls. Using faulty controls is a well-known source of erroneous conclusions, and blood donors are generally favored for blood group O.^[3,21] Two of the four studies reported that some primary studies used blood donors as controls, while the remaining studies did not report this information.

Furthermore, in many small meta-analyses, we may not accurately estimate heterogeneity.^[19,22,23]

Moreover, according to Pei et al.^[24], heterogeneity tests were frequently underpowered. As a result, the degree of heterogeneity may be greater than what is observed. Heterogeneity between studies may introduce false-positive results.

A long-standing concern about the validity of epidemiological findings has been the possibility of bias due to uncontrolled confounding. Even when known risk factors are controlled for, residual confounding may occur due to measurement error or unmeasured or unknown risk factors. Although residual confounding is challenging to eliminate in observational studies, the amount of information that this unknown confounding can explain is limited.^[25] In addition, according to Dekkers et al.,^[26] confounding is a significant threat to the validity of observational studies. Confounding occurs when comparison groups differ in terms of their risk of developing an adverse outcome other than the exposure(s) of interest due to a common cause of exposure and outcome. If the included studies have a low risk of bias and the observed heterogeneity is small, researchers may conclude that the primary findings provide reasonably valid estimates.^[27]

The researchers emphasized the importance of investigating the reasons or sources of heterogeneity in meta-analyses, particularly those involving observational studies.^[27-29]

Despite significant heterogeneity across studies, only 50% of the studies included in this umbrella review used subgroup analysis to investigate the sources of heterogeneity. Only 25% of the studies included in this umbrella review provided information about the participants’ age, gender, and race and investigated the association between Rh-type COVID-19 infection.

Although the mechanisms underlying the association between ABO blood groups and risk of COVID-19 infection and severity are not fully understood, several hypotheses regarding mechanisms linking Non-O blood group type increased susceptibility and severity compared to O blood group type can be advanced:

1. O blood group has anti-A antibodies,^[5,16] which can target ACE2 and prevent cellular entry of SARS-COV-2, led to COVID-19 infection.

2. The non-O blood group has higher furin (a cellular enzyme pertaining to the category of proprotein convertases) levels, which allow more activation of COVID-19 infection.

3. Non-O blood group, especially A group have a higher body mass index.

4. The hypercoagulable state is more seen in the non-O blood group.

5. Upregulated inflammation autoimmunity is more prevalent in the non-O blood group.^[30-32]

6. Non-O blood group intestinal microbiota (Gut microbiota) are directed toward actinobacteria (pro-inflammatory).^[33]

7. We added the diathesis-stress model, also known as the vulnerability-stress model, which is a psychological theory of psychopathology to explain our results regarding the association between ABO blood groups and the risk of COVID-19 infection and severity; however, further investigations are needed to test this model not only on psychopathology but also on various infectious and other noninfectious diseases. In other words, this model is applicable to all health conditions and problems.

The diathesis-stress model argues that certain pathological states or diseases emerge from the combination or interaction of predisposition (diathesis) with stressful events (stress). This model specifies that neither the diathesis nor stress alone is sufficient to produce the disorder. Instead, stress activates the diathesis, which then leads to the disorder. More broadly, the diathesis-stress models are similar to the idea of risk factors for stress-related diseases.^[34] The stress-diathesis model recognizes that people have their unique diathesis (vulnerabilities and protections) to the experience of trauma, positioning these as pre-and-post-trauma experiences in a bio-psycho-socio-spiritual context.^[35]

The biopsychosocial model was first conceptualized by Engle in 1977, suggesting that to understand a person’s medical condition, it is not simply the biological factors to consider but also the psychological and social factors.^[36]

Biological factors such as genetic vulnerability, physical health, and comorbidities. Psychological factors such as perceived stress, psychological distress, psychological health, self-esteem, coping skills, cognitions, emotions, behaviors, lifestyle and psychological resilience, and social factors such as social networks, social support, and socioeconomic status. The interaction of biopsychosocial factors may lead to a specific health condition. The COVID-19 pandemic is a traumatic or severe stressful situation, which may lead to both adverse psychosocial consequences and COVID-19 infection, severity, and mortality due to impaired immune system through feedback loop in vulnerable people with non-O blood group. The interaction of psychological and biological resiliency may lead to “bio-psycho-neuro-immunological resiliency.” We believe that the diathesis-stress model may be potentially most helpful in explaining all human health conditions and problems, including COVID-19 and long COVID or post-COVID-19 conditions, infectious and non-infectious diseases. The COVID-19 pandemic is a traumatic or severe stressful situation, which may lead to both adverse psychological consequences and COVID-19 infection, severity, and mortality due to impairment and dysregulation of immune system through feedback loop in vulnerable people with non-o blood group.

The interaction of psychological and biological resiliency may lead to “bio-psycho-neuro-immunological resiliency”.

The diathesis-stress model may be potentially most helpful in explaining all human health conditions and problems, including COVID-19 and Long COVID or Post-COVID-19 condition, infectious and non—infectious diseases.

Because based on a biopsychological viewpoint as a holistic or systemic approach, there is no purely medical or biological health conditions in human being.

Although key stress hormones such as norepinephrine and glucocorticoids such as cortisol have been implicated in these processes, a direct link between the brain cells that coordinate the neuroendocrine stress response has remained elusive.^[37] Bains and Sharkey^[37] showed that in comparison to age-and-sex-matched controls, exposed to SARS-CoV-2 stressed mice had higher viral titers. These effects were also dependent on corticosterone. Furthermore, this attenuation of the response to the virus is not specific to SARS-CoV-2, as stress also increases viral titers after exposure to the influenza virus. Considerable data suggest that early-life and chronic stress, as well as acute stress, dysregulate both innate and adaptive immune systems by altering the balance of cytokines toward an inflammatory milieu. Resilient individuals have different innate and adaptive immunophenotypes from that of stress-sensitive individuals, but there remains a paucity of studies that directly assess the role of brain circuitry involved in these differences. The main lesson is that stress during the early phase of virus exposure impairs host adaptive immunity against infections.

Researchers from the University of Texas Health Science Center at San Antonio, working with collaborators in five countries, revealed that the capacity to resist or recover from infections and other sources of inflammatory stress, called “immune resilience,” differs widely among individuals. They also found that immune resilience is not age-dependent. Immune resilience is the capacity to maintain good immune function, called “immunocompetence,” and minimize inflammation while experiencing inflammatory stressors. They also showed that individuals with optimal levels of immune resilience were more likely to live longer, resist HIV and influenza infections, resist recurrence of skin cancer after kidney transplant, and survive COVID-19 infection and sepsis. However, the concept of immune resilience captures levels of immunocompetence and inflammation together.^[38]

Abegaz^[39], in a review, found that higher incidence of various types of cancers in the stomach, ovaries, salivary glands, cervix, uterus, and colon/rectal and thromboembolic diseases were more common in blood type A people than in O type people and concluded that it is now clear that ABO blood types are not the exact cause of diseases, but they can be susceptible and surrender to disease and health problems. In general, non-O blood types are more susceptible to disease than O.

In general, the evidence mentioned above appears to be in favor of the diathesis-stress model.

The following are some of the potential limitations of this umbrella review: First, due to the small sample size, the findings were limited. Second, this study’s omission of gray literature is another limitation.

However, this umbrella review’s strengths include searching multiple databases and limiting itself to peer-reviewed articles.

CONCLUSION

This umbrella review indicates that blood group A may be a risk factor for COVID-19 infection, whereas blood group O may be a protective factor. However, most of the studies included in this umbrella review reported significant heterogeneity across primary studies, limiting the findings. In addition, the mechanisms of the association between ABO blood groups and the risk of COVID-19 infection are not fully understood. Therefore, additional methodologically rigorous and experimental research, as well as prospective cohorts, are warranted regarding ABO blood group and COVID-19 infection, severity, and demise.