COVID-19 Susceptibility By Race, Blood Type

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COVID-19 Susceptibility By Race, Blood Type Jun 22, 2020 4:43:44 GMT

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Post by Admin on Jun 22, 2020 4:43:44 GMT

Ethnicity, critical care admission and invasive mechanical ventilation
Overall, 4,353 patients (14%) were admitted to a critical care facility (figure 4A). On unadjusted analysis, White individuals (n=3,258, 13%) were less likely to be admitted to critical care than South Asian (293, 21%), East Asian (64, 24%), Black (241, 22%), or Other Ethnic Minority (497, 21%) people.

In models accounting for age, sex, and location (figure 5B), these associations persisted in South Asian (odds ratio 1.28, 95% confidence interval 1.09 to 1.59), Black (1.36, 1.14 to 1.62), and Other Ethnic Minority (1.29, 1.13 to 1.47) (table E3) groups. No meaningful change in these estimates was seen with the sequential introduction of potential mediators including deprivation and comorbidities (diabetes, obesity, chronic cardiac disease, non-asthmatic chronic pulmonary disease, chronic kidney disease, and dementia) (figure
5C).

Similar findings were found in analyses of invasive mechanical ventilation (Figure E2, table E3).

Ethnicity and survival from COVID-19
In analyses not accounting for age and sex differences between ethnic groups, in-hospital mortality occurred more frequently in the White group compared to the Ethnic Minorities (figure 4C, 6A). A sensitivity analysis excluding patients admitted in the most recent four weeks showed the same results (figure 4D).

However, in models accounting for age, sex, and location, evidence of higher mortality was seen in South Asian (hazard ratio 1.19, 1.05 to 1.36), but not East Asian (1.00, 0.74 to 1.35), Black (1.05, 0.91 to 1.21) or Other Ethnic Minority (0.99, 0.89 to 1.10) groups, compared to White group (figure 6B; table E5). No interaction was seen between ethnicity and age nor ethnicity and sex. Similar results were seen in alternative models of competing risks (table E10) and in models replaced in the ten multiple imputation sets (figure E3).

All associated comorbidities were explored as potential mediators of the apparent association between South Asian ethnicity and an increased hazard of death. Comorbidities associated with death in age- and sexadjusted analysis were explored (table E11 to E15). Of these, diabetes (hazard ratio 2.29, 1.99 to 2.62, P < 0.001) and chronic kidney disease (1.66, 1.38 to 2.00, P < 0.001) had strong positive associations with South Asian ethnicity (figure 2).

A significant mediation effect (total natural indirect) of diabetes was found (hazard ratio 1.03, 1.02 to 1.04, P < 0.001; table E16) representing 17.8% (8.9 to 65.7, P = 0.009; table E17) of the total effect of South Asian ethnicity on mortality (figure 7). Chronic kidney disease did not contribute significantly beyond the addition of diabetes to the model. Finally, to explore the relationship between age, diabetes and South Asian ethnicity, a Cox model was fitted including a three-way interaction (Figure 7B). The excess mortality was seen only in older South Asian patients. The association between diabetes and mortality was strongest in younger patients, compared with older. The diabetes effect was as strong in white patients, but the
prevalence of diabetes higher in the South Asian group.

As reported in our earlier analysis, extreme elderly age and the presence of comorbidities were associated
with a lower likelihood of critical care admission, suggesting advanced care planning decisions on the ward.4
While this may have disproportionately affected patients in the White group who were significantly older
with more cardiac and respiratory disease, the increased likelihood of critical care admission and IMV in
Ethnic Minorities persisted after adjustment. This may reflect an increased severity of disease in multiple
Ethnic Minority populations, but falling short of significantly increased mortality in many.

Our finding of significantly increased mortality in the South Asian group is in agreement with reports which
found that people from Asian and Black ethnic groups were at increased risk of in-hospital death from
COVID-19, and that this was only partially attributable to pre-existing clinical risk factors or deprivation.8,18,35

We add an important and detailed picture of differences in characteristics of ethnic groups in hospital. The
prevalence of diabetes is also striking at around 40% in South Asian and Black patients and the mediation of
the mortality effect by diabetes is important. This appeared independent of chronic cardiac disease and
chronic kidney disease and is likely to be due to the multitude of other negative health associations carried
by the condition.

What of the increased mortality in South Asian patients left unexplained? Our models did not account well
for wide socioeconomic determinants of outcome, as well as nuance in the comorbidity data. Socioeconomic
inequalities contribute to differential exposure to infection, differential vulnerability following infection, and
differential consequences of infection as well as control measures.36 In other studies, at Local Authority
district level, the districts with a greater proportion of residents from non-White groups experienced higher
COVID19 mortality rates, as did districts with a greater proportion of residents experiencing deprivation
from low income.19 We did not find that deprivation at the level of the hospital was a significant predictor of
time to hospital admission or severity of illness in hospital, however, aggregation of deprivation at hospital
level masks individual socioeconomic effects. Future studies should examine the disaggregated effects
where possible.

Why was excess mortality not apparent among our Black population, as others have reported?8 It seems
unlikely this group are dying disproportionately in community compared with hospital settings, so any true
effect should be visible in our cohort. Given that our study includes 40% of the UK hospitalised population,
selection bias is clearly a possibility. On the other hand, it is possible that the estimates in our study are
correct, given that some population-level reports rely on older census data as the denominator. Our ongoing
work includes linking our dataset widely to better understand the place of the in-hospital stay as part
of the full patient journey.

Last Edit: Jun 23, 2020 6:58:48 GMT by Admin

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COVID-19 Susceptibility By Race, Blood Type Jun 23, 2020 6:53:49 GMT

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Genes associated with certain blood types may increase the risk of severe COVID-19 infections, leading to respiratory failure and death, a new study suggests.

The study authors found that people with blood type A were 50% more likely than people with other blood types to experience severe COVID-19 symptoms and respiratory failure. By comparison, people with blood type O had a 50% reduced risk of developing severe symptoms of COVID-19 — the disease caused by the novel coronavirus — or those severe enough to require oxygen or a ventilator.

Scientists uncovered the connection between blood type and COVID-19 outcome using a genome-wide association study. By looking at the single-letter changes in many genes across a large population, researchers can pinpoint gene variants that may be tied to disease risk, according to the National Human Genome Research Institute.

Two prior studies have hinted at the possibility of a link between blood types and risk factors for COVID-19, Live Science previously reported. Different teams of researchers found that people with blood type A had a higher risk of developing COVID-19, compared with people who had other blood types, and that people with blood type O were less likely to contract the disease.

However, these studies were released on the preprint database medRxiv — on March 27 and on April 11 — and were not peer-reviewed1,2.

1. www.medrxiv.org/content/10.1101/2020.03.11.20031096v2

2. www.medrxiv.org/content/10.1101/2020.04.08.20058073v1.full.pdf

In the new study, researchers identified two regions in the genome where genetic variants were linked to severe cases of COVID-19 and a higher risk of death; in one of these regions was a gene that determines blood type. They published their findings online June 17 in the New England Journal of Medicine.

The researchers sampled the genomes of 1,610 COVID-19 patients and more than 1,300 healthy blood donors from Italy and Spain, and analyzed more than 8 million single-letter changes in the DNA code, called single nucleotide polymorphisms, or SNPs (pronounce "snips"). There are millions of SNPs sprinkled throughout a person's genome, and they can be used as markers for locating genes associated with disease, according to the U.S. National Library of Medicine.

Genomewide Association Study of Severe Covid-19 with Respiratory Failure
List of authors.
David Ellinghaus, Ph.D., Frauke Degenhardt, M.Sc., Luis Bujanda, M.D., Ph.D., Maria Buti, M.D., Ph.D., Agustín Albillos, M.D., Ph.D., Pietro Invernizzi, M.D., Ph.D., Javier Fernández, M.D., Ph.D., Daniele Prati, M.D., Guido Baselli, Ph.D., Rosanna Asselta, Ph.D., Marit M. Grimsrud, M.D., Chiara Milani, Ph.D., et al., for The Severe Covid-19 GWAS Group*

Abstract
BACKGROUND
There is considerable variation in disease behavior among patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes coronavirus disease 2019 (Covid-19). Genomewide association analysis may allow for the identification of potential genetic factors involved in the development of Covid-19.

METHODS
We conducted a genomewide association study involving 1980 patients with Covid-19 and severe disease (defined as respiratory failure) at seven hospitals in the Italian and Spanish epicenters of the SARS-CoV-2 pandemic in Europe. After quality control and the exclusion of population outliers, 835 patients and 1255 control participants from Italy and 775 patients and 950 control participants from Spain were included in the final analysis. In total, we analyzed 8,582,968 single-nucleotide polymorphisms and conducted a meta-analysis of the two case–control panels.

RESULTS
We detected cross-replicating associations with rs11385942 at locus 3p21.31 and with rs657152 at locus 9q34.2, which were significant at the genomewide level (P<5×10−8) in the meta-analysis of the two case–control panels (odds ratio, 1.77; 95% confidence interval [CI], 1.48 to 2.11; P=1.15×10−10; and odds ratio, 1.32; 95% CI, 1.20 to 1.47; P=4.95×10−8, respectively). At locus 3p21.31, the association signal spanned the genes SLC6A20, LZTFL1, CCR9, FYCO1, CXCR6 and XCR1. The association signal at locus 9q34.2 coincided with the ABO blood group locus; in this cohort, a blood-group–specific analysis showed a higher risk in blood group A than in other blood groups (odds ratio, 1.45; 95% CI, 1.20 to 1.75; P=1.48×10−4) and a protective effect in blood group O as compared with other blood groups (odds ratio, 0.65; 95% CI, 0.53 to 0.79; P=1.06×10−5).

CONCLUSIONS
We identified a 3p21.31 gene cluster as a genetic susceptibility locus in patients with Covid-19 with respiratory failure and confirmed a potential involvement of the ABO blood-group system. (Funded by Stein Erik Hagen and others.)

www.nejm.org/doi/full/10.1056/NEJMoa2020283

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COVID-19 Susceptibility By Race, Blood Type Jun 24, 2020 5:37:40 GMT

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Post by Admin on Jun 24, 2020 5:37:40 GMT

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was discovered in Wuhan, China, in late 2019, and coronavirus disease 2019 (Covid-19), the disease caused by SARS-CoV-2, rapidly evolved into a global pandemic.1 As of June 15, 2020, there

were more than 8.03 million confirmed cases worldwide, with total deaths exceeding 436,900.2 In Europe, Italy and Spain were severely affected early on, with epidemic peaks starting in the second half of February 2020 (Figure 1) and 61,507 deaths reported by June 15, 2020. Covid-19 has varied manifestations,3 with the large majority of infected persons having only mild symptoms or even no symptoms.4 Mortality rates are driven predominantly by the subgroup of patients who have severe respiratory failure related to interstitial pneumonia in both lungs and acute respiratory distress syndrome.5 Severe Covid-19 with respiratory failure requires early and prolonged support by mechanical ventilation.6

The pathogenesis of severe Covid-19 and the associated respiratory failure is poorly understood, but higher mortality is consistently associated with older age and male sex.7,8 Clinical associations have also been reported for hypertension, diabetes, and other obesity-related and cardiovascular disease traits, but the relative role of clinical risk factors in determining the severity of Covid-19 has not yet been clarified.7-11 Observational data on lymphocytic endotheliitis and diffuse microvascular and macrovascular thromboembolic complications suggest that Covid-19 is a systemic disease that involves injury to the vascular endothelium but provide little insight into the underlying pathogenesis.12-14 At the peak of the epidemic in Italy and Spain in early 2020, we performed a genomewide association study (GWAS) in an attempt to delineate host genetic factors contributing to severe Covid-19 with respiratory failure. The relatively low disease burden of Covid-19 in Norway and Germany allowed for a complementary team to be set up, whereby genotyping and analysis could occur in parallel with the rapid recruitment of patients in the heavily affected Italian and Spanish epicenters.

Results

Table 1.

Overview of Patients Included in the Final Analysis.
The milestones of the study in the context of the peak outbreaks in Italy and Spain are shown in Figure 1. Data on the age, sex, maximum respiratory support at any point during hospitalization, and relevant coexisting conditions (type 2 diabetes, hypertension, and coronary heart disease) in the patients who were included in the final analysis are shown in Table 1 and in Table S2 in Supplementary Appendix 1. Because we used the same genotyping platform (GSA) to obtain both data sets, we were able to perform a uniform quality control of the merged Italian and Spanish SNP data sets, thus reducing technical confounders to a minimum. A quantile–quantile (Q-Q) plot of the two meta-analyses (the main analysis and the analysis corrected for age and sex) showed significant associations in the tail of the distribution with minimal genomic inflation (λGC=1.015 for main analysis and λGC=1.006 for analysis corrected for age and sex) (Fig. S2 in Supplementary Appendix 1). We also carried out separate association analyses for the Italian and Spanish data sets (see the Supplementary Methods section and Fig. S3).

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COVID-19 Susceptibility By Race, Blood Type Jun 24, 2020 8:02:53 GMT

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GENOMEWIDE ASSOCIATION ANALYSIS

Figure 2.

GWAS Summary (Manhattan) Plot of the Meta-analysis Association Statistics Highlighting Two Susceptibility Loci with Genomewide Significance for Severe Covid-19 with Respiratory Failure.

Table 2.

Susceptibility Loci Associated with Severe Covid-19 with Respiratory Failure.
We found two loci to be associated with Covid-19–induced respiratory failure with genomewide significance (P<5×10−8) in the main meta-analysis: the rs11385942 insertion–deletion GA or G variant at locus 3p21.31 (odds ratio for the GA allele, 1.77; 95% confidence interval [CI], 1.48 to 2.11; P=1.15×10−10) and the rs657152 A or C SNP at locus 9q34.2 (odds ratio for the A allele, 1.32; 95% CI, 1.20 to 1.47; P=4.95×10−8) (Figure 2 and Table 2 and Supplementary Appendix 2, available at NEJM.org). Both loci showed nominally significant association in both the Spanish and Italian subanalyses (Table 2). The meta-analysis association results for recessive and heterozygous genetic models for the two meta-analyses (main analysis and the analysis corrected for age and sex) are provided in Supplementary Appendix 3, available at NEJM.org. The imputation quality of the associated markers was good (Table 2 and Supplementary Appendix 2), and manual inspection of genotype cluster plots of genotyped SNPs in these regions showed distinct genotype clouds for homozygous and heterozygous calls (Fig. S4 in Supplementary Appendix 1). Furthermore, the analyses that were corrected for age and sex corroborated the observations at both rs11385942 (meta-analysis odds ratio, 2.11; 95% CI, 1.70 to 2.61; P=9.46×10−12) and rs657152 (meta-analysis odds ratio, 1.39; 95% CI, 1.22 to 1.59; P=5.35×10−7) (Table 2 and Fig. S5 in Supplementary Appendix 1).

The allele frequencies in Spanish and Italian control data sets from previously published studies21-27 are consistent with those we report here (Supplementary Appendix 2). A further 24 different genomic loci showed suggestive evidence (P<1×10−5) for association with Covid-19–induced respiratory failure in the main analysis (Supplementary Appendix 4, available at NEJM.org, and Fig. S6 in Supplementary Appendix 1). Association signals at loci 3p21.31 and 9q34.2 were fine-mapped to 22 and 38 variants, respectively, with greater than 95% certainty (Figure 3A and 3B and Supplementary Appendix 5, available at NEJM.org).

Last Edit: Jun 24, 2020 20:40:50 GMT by Admin

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COVID-19 Susceptibility By Race, Blood Type Jun 24, 2020 20:40:34 GMT

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Figure 3. Regional Association Plots of Susceptibility Loci Associated with Severe Covid-19 with Respiratory Failure.
Bayesian fine-mapping analysis prioritized 22 and 38 variants for loci 3p21.31 (Panel A) and 9q34.2 (Panel B), respectively, with greater than 95% certainty. The linkage disequilibrium values were calculated on the basis of genotypes of the merged Italian and Spanish data sets derived from TOPMed (Trans-Omics for Precision Medicine) imputation. The positions in the genome assembly hg38 are plotted. The recombination rate is shown in centimorgans (cM) per million base pairs (Mb). The plot shows the names and locations of the genes; the transcribed strand is indicated with an arrow. Genes are represented with intronic and exonic regions. The purple diamond in each panel represents the variant most strongly associated with severe Covid-19 and respiratory failure.

CHROMOSOME 3P21.31
The association signal at locus 3p21.31 comprised six genes (SLC6A20, LZTFL1, CCR9, FYCO1, CXCR6, and XCR1) (Figure 3A). The risk allele GA of rs11385942 is associated with reduced expression of CXCR6 and increased expression of SLC6A20, and LZTFL1 is strongly expressed in human lung cells (Fig. S7 and Supplementary Appendix 6, available at NEJM.org). We found that the frequency of the risk allele of the lead variant at 3p21.31 (rs11385942) was higher among patients who received mechanical ventilation than among those who received oxygen supplementation only in both the main meta-analysis (odds ratio, 1.70; 95% CI, 1.27 to 2.26; P=3.30×10−4) and the meta-analysis corrected for age and sex (odds ratio, 1.56; 95% CI, 1.17 to 2.01; P=0.003) (Supplementary Appendix 7, available at NEJM.org). Furthermore, the 19 patients who were homozygous for the rs11385942 risk allele were younger than 1591 patients who were heterozygous or homozygous for the nonrisk allele (median age, 59 years [interquartile range, 49 to 68] vs. 66 years [interquartile range, 56 to 75]; P=0.005). Available variant database entries suggest that the frequency of this risk allele varies among populations worldwide (Fig. S8 in Supplementary Appendix 1).

ABO LOCUS
At locus 9q34.2 the association signal coincided with the ABO blood group locus (Figure 3B and Fig. S9 in Supplementary Appendix 1). Accordingly, the distribution of ABO blood groups (predicted from combinations of genotypes of three different SNPs) was skewed among patients with Covid-19 who had respiratory failure, as compared with the distribution among control participants. In the meta-analysis corrected for age and sex, we found a higher risk among persons with blood group A than among patients with other blood groups (odds ratio, 1.45; 95% CI, 1.20 to 1.75; P=1.48×10−4) and a protective effect for blood group O as compared with the other blood groups (odds ratio, 0.65; 95% CI, 0.53 to 0.79; P=1.06×10−5). Details are provided in Supplementary Appendix 8, available at NEJM.org. Both associations and effect directions were consistent in the separate Spanish and Italian case–control analyses. We found no significant difference in blood-group distribution between patients receiving supplemental oxygen only and those receiving mechanical ventilation of any kind. The ABO blood-group frequency distributions in public registries are provided for comparison in Supplementary Appendix 8, along with details of the results presented here, and corroborate our observations.

HLA ANALYSIS
Given its important role in several viral infections, we scrutinized the extended HLA region (chromosome 6, 25 through 34 Mb). There were no SNP association signals at the HLA complex that met even the significance threshold of suggestive association: P<1×10−5 (Fig. S10 in Supplementary Appendix 1). Dedicated analysis of the classical HLA loci showed no significant allele associations with either Covid-19 or disease severity (oxygen supplementation only or mechanical ventilation of any kind), and further analysis of heterozygote and divergent allele advantage or predicted number of HLA-bound SARS-CoV-2 peptides did not show significant associations with Covid-19 in this data set (see the HLA Analyses section in Supplementary Appendix 1 and Supplementary Appendix 9, available at NEJM.org).

Using a pragmatic approach with simplified inclusion criteria and a complementary team of clinicians at the European Covid-19 epicenters in Italy and Spain and scientists in the less-burdened countries of Germany and Norway, we performed a GWAS that included de novo genotyping for Covid-19 with respiratory failure in approximately 2 months. We detected a novel susceptibility locus at a chromosome 3p21.31 gene cluster and confirmed a potential involvement of the ABO blood-group system in Covid-19.

On chromosome 3p21.31, the peak association signal covered a cluster of six genes (SLC6A20, LZTFL1, CCR9, FYCO1, CXCR6, and XCR1), several of which have functions that are potentially relevant to Covid-19. A causative gene cannot be reliably implicated by the present data. One candidate is SLC6A20, which encodes the sodium–imino acid (proline) transporter 1 (SIT1) and which functionally interacts with angiotensin-converting enzyme 2, the SARS-CoV-2 cell-surface receptor.28,29 However, the locus also contains genes encoding chemokine receptors, including the CC motif chemokine receptor 9 (CCR9) and the C-X-C motif chemokine receptor 6 (CXCR6), the latter of which regulates the specific location of lung-resident memory CD8 T cells throughout the sustained immune response to airway pathogens, including influenza viruses.30 Flanking genes (e.g., CCR1 and CCR2) also have relevant functions,31 and further studies will be needed to delineate the functional consequences of detected associations.

The preliminary results from the Covid-19 Host Genetics Consortium32 include suggestive associations within the same locus at chromosome 3p21.31, which lend considerable support to our findings (Fig. S11 in Supplementary Appendix 1). The consortium analysis also used population-based controls, but the patients included persons with mild Covid-19 and those with severe Covid-19. The parallel findings nevertheless underscore an important point about the ascertainment of patients and controls in genetic studies of Covid-19. Because the majority of patients with SARS-CoV-2 infection are asymptomatic, any sample involving patients with a positive nasopharyngeal RNA test is likely to hold a bias toward some degree of symptomatic burden. Two of the identifiers for inclusion in the current study were a positive result for the presence of SARS-CoV-2 according to PCR testing and receipt of respiratory support (an extreme Covid-19 phenotype). As such, it seems reasonable to conclude that the chromosome 3p21.31 locus is involved in Covid-19 susceptibility per se, with a possible enrichment in patients with severe disease. This latter interpretation is supported by the significantly higher frequency of the risk allele among patients who received mechanical ventilation than among those who received supplemental oxygen only as well as by the finding of younger age among patients who were homozygous for the risk allele than among patients who were heterozygous or homozygous for the nonrisk allele.

Nongenetic studies that were reported as preprints33,34 have previously implicated the involvement of ABO blood groups in Covid-19 susceptibility, and ABO blood groups have also been implicated in susceptibility to SARS-CoV-1 infection.35 Our genetic data confirm that blood group O is associated with a risk of acquiring Covid-19 that was lower than that in non-O blood groups, whereas blood group A was associated with a higher risk than non-A blood groups.33,34 The biologic mechanisms undergirding these findings may have to do with the ABO group per se (e.g., with the development of neutralizing antibodies against protein-linked N-glycans)36 or with other biologic effects of the identified variant,37-39 including the stabilization of von Willebrand factor.40,41 The ABO locus holds considerable risk for population stratification,42 which is increased by the inclusion of randomly selected blood donors in the current study (for which there is an inherent risk of blood group O enrichment). Alignment of the allele frequencies at the ABO locus in our control population with those in several non–blood-donor control populations would suggest that this is not a major bias, and at least one study34 that tested for association with blood type used disease controls with no affiliation to blood donors.

The pragmatic aspects leading to the feasibility of this massive undertaking in a very short period of time during the extreme clinical circumstances of the pandemic imposed limitations that will be important to explore in follow-up studies. For example, to enable the recruitment of study participants, a bare minimum of clinical metadata was requested. For this reason, extensive genotype–phenotype elaboration of current findings could not be conducted, and adjustments for all potential sources of bias (e.g., underlying cardiovascular and metabolic factors relevant to Covid-19) could not be performed. Furthermore, we have limited information about the SARS-CoV-2 infection status in the control participants; this concern is mitigated by the fact that the presence of susceptible persons in the control group would only bias the tests toward the null. In addition, few restrictions were imposed during inclusion, which led to genotyped samples having to be excluded owing to differing ethnic groups (population outliers). Further exploration of current findings, both as to their usefulness in clinical risk profiling of patients with Covid-19 and toward a mechanistic understanding of the underlying pathophysiology, is warranted.

June 17, 2020
DOI: 10.1056/NEJMoa2020283