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IntroductionSepsis is characterised by dysregulated, life-threatening immune responses, which are thought to be driven by cytokines such as interleukin-6 (IL-6). Genetic variants in IL6R known to downregulate IL-6 signalling are associated with improved COVID-19 outcomes, a finding later confirmed in randomised trials of IL-6 receptor antagonists (IL6RA). We hypothesised that blockade of IL6R could also improve outcomes in sepsis. MethodsWe performed a Mendelian randomisation analysis using single nucleotide polymorphisms (SNPs) in and near IL6R to evaluate the likely causal effects of IL6R blockade on sepsis, sepsis severity, other infections, and COVID-19. We weighted SNPs by their effect on CRP and combined results across them in inverse variance weighted meta-analysis, proxying the effect of IL6RA. Our outcomes were measured in UK Biobank, FinnGen, the COVID-19 Host Genetics Initiative (HGI), and the GenOSept and GainS consortium. We performed several sensitivity analyses to test assumptions of our methods, including utilising variants around CRP in a similar analysis. ResultsIn the UK Biobank cohort (N=485,825, including 11,643 with sepsis), IL6R blockade was associated with a decreased risk of sepsis (OR=0.80; 95% CI 0.66-0.96, per unit of natural log transformed CRP decrease). The size of this effect increased with severity, with larger effects on 28-day sepsis mortality (OR=0.74; 95% CI 0.38-0.70); critical care admission with sepsis (OR=0.48, 95% CI 0.30-0.78) and critical care death with sepsis (OR=0.37, 95% CI 0.14 - 0.98) Similar associations were seen with severe respiratory infection: OR for pneumonia in critical care 0.69 (95% CI 0.49 - 0.97) and for sepsis survival in critical care (OR=0.22; 95% CI 0.04- 1.31) in the GainS and GenOSept consortium. We also confirm the previously reported protective effect of IL6R blockade on severe COVID-19 (OR=0.69, 95% 0.57 - 0.84) in the COVID-19 HGI, which was of similar magnitude to that seen in sepsis. Sensitivity analyses did not alter our primary results. ConclusionsIL6R blockade is causally associated with reduced incidence of sepsis, sepsis related critical care admission, and sepsis related mortality. These effects are comparable in size to the effect seen in severe COVID-19, where IL-6 receptor antagonists were shown to improve survival. This data suggests a randomised trial of IL-6 receptor antagonists in sepsis should be considered.
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The COVID-19 pandemic negatively impacted mental health globally. Individuals with neurodevelopmental disorders (NDDs), including autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD), are at elevated risk of mental health difficulties. Therefore, we investigated the impact of the pandemic on anxiety, depression and mental wellbeing in adults with NDDs using longitudinal data from the Avon Longitudinal Study of Parents and Children study (n=3,058). Mental health data were collected pre-pandemic (age 21-25) and at three timepoints during the pandemic (ages 27-28) using the Short Mood and Feelings Questionnaire, Generalised Anxiety Disorder Assessment-7, and Warwick Edinburgh Mental Wellbeing Scale. ADHD and ASD were defined using validated cut-points of the Strengths and Difficulties Questionnaire and Autism Spectrum Quotient, self-reported at age 25. We used multi-level mixed-effects models to investigate changes in mental health in those with ADHD and ASD compared to those without. Prevalences of depression, anxiety and poor mental wellbeing were higher at all timepoints (pre-pandemic and during pandemic) in those with ADHD and ASD compared to those without. Anxiety increased to a greater extent in those with ADHD ({beta}=0.8 [0.2,1.4], p=0.01) and ASD ({beta}=1.2 [-0.1,2.5], p=0.07), while depression symptoms decreased, particularly in females with ASD ({beta}=-3.1 [-4.6,-1.5], p=0.0001). On average, mental wellbeing decreased in all, but to a lesser extent in those with ADHD ({beta}=1.3 [0.2,2.5], p=0.03) and females with ASD ({beta}=3.0 [0.2,5.9], p=0.04). To conclude, anxiety disproportionately increased in adults with NDDs during the pandemic, however, the related lockdowns may have provided a protective environment for depressive symptoms in the same individuals.
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BackgroundStructural barriers to testing may introduce selection bias in COVID-19 research. We explore whether changes to testing and lockdown restrictions introduce time-specific selection bias into analyses of socioeconomic position (SEP) and SARS-CoV-2 infection. MethodsUsing UK Biobank (N = 420 231; 55 % female; mean age = 56{middle dot}3 [SD=8{middle dot}01]) we estimated the association between SEP and i) being tested for SARS-CoV-2 infection versus not being tested ii) testing positive for SARS-CoV-2 infection versus testing negative and iii) testing negative for SARS-CoV-2 infection versus not being tested, at four distinct time-periods between March 2020 and March 2021. We explored potential selection bias by examining the same associations with hypothesised positive (ABO blood type) and negative (hair colour) control exposures. Finally, we conducted a hypothesis-free phenome-wide association study to investigate how individual characteristics associated with testing changed over time. FindingsThe association between low SEP and SARS-CoV-2 testing attenuated across time-periods. Compared to individuals with a degree, individuals who left school with GCSEs or less had an OR of 1{middle dot}05 (95% CI: 0{middle dot}95 to 1{middle dot}16) in March-May 2020 and 0{middle dot}98 (95% CI: 0{middle dot}94 to 1{middle dot}02) in January-March 2021. The magnitude of the association between low SEP and testing positive for SARS-CoV-2 infection increased over the same time-period. For the same comparisons, the OR for testing positive increased from 1{middle dot}27 (95% CI: 1{middle dot}08 to 1{middle dot}50), to 1{middle dot}73 (95% CI: 1{middle dot}59 to 1{middle dot}87). We found little evidence of an association between both control exposures and all outcomes considered. Our phenome-wide analysis highlighted a broad range of individual traits were associated with testing, which were distinct across time-periods. InterpretationThe association between SEP (and indeed many individual traits) and SARS-CoV-2 testing changed over time, indicating time-specific selection pressures in COVID-19. However, positive, and negative control analyses suggest that changes in the magnitude of the association between SEP and SARS-CoV-2 infection over time were unlikely to be explained by selection bias and reflect true increases in socioeconomic inequalities. FundingUniversity of Bristol; UK Medical Research Council; British Heart Foundation; European Union Horizon 2020; Wellcome Trust and The Royal Society; National Institute of Health Research; UK Economic and Social Research Council
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ObjectiveTo use the example of the effect of body mass index (BMI) on COVID-19 susceptibility and severity to illustrate methods to explore potential selection and misclassification bias in Mendelian randomisation (MR) of COVID-19 determinants. DesignTwo-sample MR analysis. SettingSummary statistics from the Genetic Investigation of ANthropometric Traits (GIANT) and COVID-19 Host Genetics Initiative (HGI) consortia. Participants681,275 participants in GIANT and more than 2.5 million people from the COVID-19 HGI consortia. ExposureGenetically instrumented BMI. Main outcome measuresSeven case/control definitions for SARS-CoV-2 infection and COVID-19 severity: very severe respiratory confirmed COVID-19 vs not hospitalised COVID-19 (A1) and vs population (those who were never tested, tested negative or had unknown testing status (A2)); hospitalised COVID-19 vs not hospitalised COVID-19 (B1) and vs population (B2); COVID-19 vs lab/self-reported negative (C1) and vs population (C2); and predicted COVID-19 from self-reported symptoms vs predicted or self-reported non-COVID-19 (D1). ResultsWith the exception of A1 comparison, genetically higher BMI was associated with higher odds of COVID-19 in all comparison groups, with odds ratios (OR) ranging from 1.11 (95%CI: 0.94, 1.32) for D1 to 1.57 (95%CI: 1.57 (1.39, 1.78) for A2. As a method to assess selection bias, we found no strong evidence of an effect of COVID-19 on BMI in a no-relevance analysis, in which COVID-19 was considered the exposure, although measured after BMI. We found evidence of genetic correlation between COVID-19 outcomes and potential predictors of selection determined a priori (smoking, education, and income), which could either indicate selection bias or a causal pathway to infection. Results from multivariable MR adjusting for these predictors of selection yielded similar results to the main analysis, suggesting the latter. ConclusionsWe have proposed a set of analyses for exploring potential selection and misclassification bias in MR studies of risk factors for SARS-CoV-2 infection and COVID-19 and demonstrated this with an illustrative example. Although selection by socioeconomic position and arelated traits is present, MR results are not substantially affected by selection/misclassification bias in our example. We recommend the methods we demonstrate, and provide detailed analytic code for their use, are used in MR studies assessing risk factors for COVID-19, and other MR studies where such biases are likely in the available data. SummaryO_ST_ABSWhat is already known on this topicC_ST_ABS- Mendelian randomisation (MR) studies have been conducted to investigate the potential causal relationship between body mass index (BMI) and COVID-19 susceptibility and severity. - There are several sources of selection (e.g. when only subgroups with specific characteristics are tested or respond to study questionnaires) and misclassification (e.g. those not tested are assumed not to have COVID-19) that could bias MR studies of risk factors for COVID-19. - Previous MR studies have not explored how selection and misclassification bias in the underlying genome-wide association studies could bias MR results. What this study adds- Using the most recent release of the COVID-19 Host Genetics Initiative data (with data up to June 2021), we demonstrate a potential causal effect of BMI on susceptibility to detected SARS-CoV-2 infection and on severe COVID-19 disease, and that these results are unlikely to be substantially biased due to selection and misclassification. - This conclusion is based on no evidence of an effect of COVID-19 on BMI (a no-relevance control study, as BMI was measured before the COVID-19 pandemic) and finding genetic correlation between predictors of selection (e.g. socioeconomic position) and COVID-19 for which multivariable MR supported a role in causing susceptibility to infection. - We recommend studies use the set of analyses demonstrated here in future MR studies of COVID-19 risk factors, or other examples where selection bias is likely.
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BackgroundObservational studies have highlighted that where individuals live is far more important for risk of dying with COVID-19, than for dying of other causes. Deprivation is commonly proposed as explaining such differences. During the period of localised restrictions in late 2020, areas with higher restrictions tended to be more deprived. We explore how this impacted the relationship between deprivation and mortality and see whether local or regional deprivation matters more for inequalities in COVID-19 mortality. MethodsWe use publicly available population data on deaths due to COVID-19 and all-cause mortality between March 2020 and April 2021 to investigate the scale of spatial inequalities. We use a multiscale approach to simultaneously consider three spatial scales through which processes driving inequalities may act. We go on to explore whether deprivation explains such inequalities. ResultsAdjusting for population age structure and number of care homes, we find highest regional inequality in October 2020, with a COVID-19 mortality rate ratio of 5.86 (95% CI 3.31 to 19.00) for the median between-region comparison. We find spatial context is most important, and spatial inequalities higher, during periods of low mortality. Almost all unexplained spatial inequality in October 2020 is removed by adjusting for deprivation. During October 2020, one standard deviation increase in regional deprivation was associated with 2.45 times higher local mortality (95% CI, 1.75 to 3.48). ConclusionsSpatial inequalities are greatest in periods of lowest overall mortality, implying that as mortality declines it does not do so equally. During the prolonged period of low restrictions and low mortality in summer 2020, spatial inequalities strongly increased. Contrary to previous months, we show that the strong spatial patterning during autumn 2020 is almost entirely explained by deprivation. As overall mortality declines, policymakers must be proactive in detecting areas where this is not happening, or risk worsening already strong health inequalities. Key messages- Spatial inequalities in local mortality are highest in periods of lower overall mortality. - Spatial inequality in COVID-19 mortality peaked in October 2020, before decreasing strongly in November and over the winter period. - Deprivation explains almost all inequality during October when inequality was at its highest. - Regional deprivation was far more strongly associated with local mortality than local deprivation during September to November 2020. - This is consistent with an overdispersed distribution of secondary infections governed by transmission heterogeneity structured by deprivation.
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BackgroundNon-random selection into analytic subsamples could introduce selection bias in observational studies of SARS-CoV-2 infection and COVID-19 severity (e.g. including only those have had a COVID-19 PCR test). We explored the potential presence and impact of selection in such studies using data from self-report questionnaires and national registries. MethodsUsing pre-pandemic data from the Avon Longitudinal Study of Parents and Children (ALSPAC) (mean age=27.6 (standard deviation [SD]=0.5); 49% female) and UK Biobank (UKB) (mean age=56 (SD=8.1); 55% female) with data on SARS-CoV-2 infection and death-with-COVID-19 (UKB only), we investigated predictors of selection into COVID-19 analytic subsamples. We then conducted empirical analyses and simulations to explore the potential presence, direction, and magnitude of bias due to selection when estimating the association of body mass index (BMI) with SARS-CoV-2 infection and death-with-COVID-19. ResultsIn both ALSPAC and UKB a broad range of characteristics related to selection, sometimes in opposite directions. For example, more educated participants were more likely to have data on SARS-CoV-2 infection in ALSPAC, but less likely in UKB. We found bias in many simulated scenarios. For example, in one scenario based on UKB, we observed an expected odds ratio of 2.56 compared to a simulated true odds ratio of 3, per standard deviation higher BMI. ConclusionAnalyses using COVID-19 self-reported or national registry data may be biased due to selection. The magnitude and direction of this bias depends on the outcome definition, the true effect of the risk factor, and the assumed selection mechanism. Key messagesO_LIObservational studies assessing the association of risk factors with SARS-CoV-2 infection and COVID-19 severity may be biased due to non-random selection into the analytic sample. C_LIO_LIResearchers should carefully consider the extent that their results may be biased due to selection, and conduct sensitivity analyses and simulations to explore the robustness of their results. We provide code for these analyses that is applicable beyond COVID-19 research. C_LI
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ImportanceThe long-term effects of COVID-19 on the incidence of vascular diseases are unclear. ObjectiveTo quantify the association between time since diagnosis of COVID-19 and vascular disease, overall and by age, sex, ethnicity, and pre-existing disease. DesignCohort study based on population-wide linked electronic health records, with follow up from January 1st to December 7th 2020. Setting and participantsAdults registered with an NHS general practice in England or Wales and alive on January 1st 2020. ExposuresTime since diagnosis of COVID-19 (categorised as 0-6 days, 1-2 weeks, 3-4, 5-8, 9-12, 13-26 and 27-49 weeks since diagnosis), with and without hospitalisation within 28 days of diagnosis. Main outcomes and measuresPrimary outcomes were arterial thromboses (mainly acute myocardial infarction and ischaemic stroke) and venous thromboembolic events (VTE, mainly pulmonary embolism and lower limb deep vein thrombosis). We also studied other vascular events (transient ischaemic attack, haemorrhagic stroke, heart failure and angina). Hazard ratios were adjusted for demographic characteristics, previous disease diagnoses, comorbidities and medications. ResultsAmong 48 million adults, 130,930 were and 1,315,471 were not hospitalised within 28 days of COVID-19. In England, there were 259,742 first arterial thromboses and 60,066 first VTE during 41.6 million person-years follow-up. Adjusted hazard ratios (aHRs) for first arterial thrombosis compared with no COVID-19 declined rapidly from 21.7 (95% CI 21.0-22.4) to 3.87 (3.58-4.19) in weeks 1 and 2 after COVID-19, 2.80 (2.61-3.01) during weeks 3-4 then to 1.34 (1.21-1.48) during weeks 27-49. aHRs for first VTE declined from 33.2 (31.3-35.2) and 8.52 (7.59-9.58) in weeks 1 and 2 to 7.95 (7.28-8.68) and 4.26 (3.86-4.69) during weeks 3-4 and 5-8, then 2.20 (1.99-2.44) and 1.80 (1.50-2.17) during weeks 13-26 and 27-49 respectively. aHRs were higher, for longer after diagnosis, after hospitalised than non-hospitalised COVID-19. aHRs were also higher among people of Black and Asian than White ethnicity and among people without than with a previous event. Across the whole population estimated increases in risk of arterial thromboses and VTEs were 2.5% and 0.6% respectively 49 weeks after COVID-19, corresponding to 7,197 and 3,517 additional events respectively after 1.4 million COVID-19 diagnoses. Conclusions and RelevanceHigh rates of vascular disease early after COVID-19 diagnosis decline more rapidly for arterial thromboses than VTEs but rates remain elevated up to 49 weeks after COVID_19. These results support continued policies to avoid COVID-19 infection with effective COVID-19 vaccines and use of secondary preventive agents in high-risk patients. Key pointsO_ST_ABSQuestionC_ST_ABSIs COVID-19 associated with higher long-term incidence of vascular diseases? FindingsIn this cohort study of 48 million adults in England and Wales, COVID-19 was associated with higher incidence, that declined with time since diagnosis, of both arterial thromboses [week 1: adjusted HR [aHR] 21.7 (95% CI 21.0-22.4) weeks 27-49: aHR 1.34 (1.21-1.48)] and venous thromboembolism [week 1: aHR 33.2 (31.3-35.2), weeks 27-49 1.80 (1.50-2.17)]. aHRs were higher, for longer, after hospitalised than non-hospitalised COVID-19. The estimated excess number of arterial thromboses and venous thromboembolisms was 10,500. MeaningAvoidance of COVID-19 infection through vaccination, and use of secondary preventive agents after infection in high-risk patients, may reduce post-COVID-19 acute vascular diseases.
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BackgroundSevere Covid19 is characterised by a hyperactive immune response. Carnitine, an essential nutrient, and its derivative acetyl-carnitine can downregulate proinflammatory cytokines and has been suggested as a potential treatment for the disease. MethodsWe carried out Mendelian randomization analyses using publicly available data from a large genome wide association study (GWAS) of metabolites and a collaborative genome wide study of Covid19 to investigate the nature of the relationship between carnitine and acetyl-carnitine and Covid19 infection, hospitalisation with Covid19 and very severe Covid19. We used the same methodology to determine whether carnitine was associated with co-morbidities commonly found among those with severe Covid19. ResultsWe found evidence of a protective effect against very severe Covid19 for both carnitine and acetyl-carnitine, with around a 40% reduction in risk associated with a doubling of carnitine or acetyl-carnitine (carnitine odds ratio (OR) = 0.56, 95% confidence intervals (CI) 0.33 to 0.95, p=0.03 and acetyl-carnitine OR=0.60, 95% CI 0.35 to 1.02, p=0.06), and evidence of protective effects on hopitalisation with Covid19. For acetyl-carnitine the largest protective effect was seen in the comparison between those hospitalised with Covid19 and those infected but not hospitalised (OR=0.34, 95%CI 0.18 to 0.62, p=0.0005). ConclusionCarnitine and acetyl-carnitine merit further investigation in respect to the prevention of severe Covid19.
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ObjectivesTo estimate occupation risk from Covid19 among teachers and others working in schools using publicly available data on mortality in England and Wales. DesignAnalysis of national death registration data from the Office for National Statistics. SettingEngland and Wales, March 20th to 28th December 2020, during the Covid19 pandemic. ParticipantsThe total working age population in England and Wales plus those still working aged over 65. Primary and Secondary outcomesDeath with Covid19 as a primary outcome and death from all causes as a secondary outcome. ResultsAcross occupational groups there was a strong correlation between Covid19 mortality and both non-Covid19 and all-cause mortality. The absolute mortality rates for deaths with Covid19 were low amongst those working in schools (from 10 per 100,000 in female primary school teachers to 39 per 100,000 male secondary school teachers) relative to many other occupations (range: 10 to 143 per 100,000 in men; 9 to 50 per 100,000 in women).There was weak evidence that secondary school teachers had slightly higher risks of dying with Covid19 compared to the average for all working aged people, but stronger evidence of a higher risk compared to the average for all professionals; primary school teachers had a lower risk. All-cause mortality was also higher amongst all teachers compared to all professionals. Teaching and lunchtime assistants were not at higher risk of death from Covid19 compared with all working aged people. ConclusionThere was weak evidence that Covid19 mortality risk for secondary school teachers was above expectation, but in general school staff had Covid19 mortality risks which were proportionate to their non-covid mortality risk. Strengths and limitations of this studyO_LIWe used routinely collected data on all deaths in England and Wales;, which are near-complete and not susceptible to serious ascertainment biases. C_LIO_LIWe were able to compare mortality data for teachers and other school workers with all other occupational groups and with the working-age population. C_LIO_LIThe number of deaths due to Covid19 were small and thus differences between the specific occupational groups were imprecisely estimated. C_LIO_LIWe did not have access to individual level mortality data so were not able to account for potential confounders such as comorbidities or household size. C_LIO_LIFor those working in school aged over 65 we had neither mortality rates per 100,000 nor total numbers within the group; we only had number of deaths and a 5-year average and we do not know whether the denominators have changed for this group over the last 5 years. C_LI
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BackgroundNumerous observational studies have highlighted structural inequalities in COVID-19 mortality in the UK. Such studies often fail to consider the complex spatial nature of such inequalities in their analysis, leading to the potential for bias and an inability to reach conclusions about the most appropriate structural levels for policy intervention. MethodsWe use publicly available population data on COVID-19 related- and all-cause mortality between March and July 2020 in England and Wales to investigate the spatial scale of such inequalities. We propose a multiscale approach to simultaneously consider four spatial scales at which processes driving inequality may act and apportion inequality between these. ResultsAdjusting for population age structure, number of care homes and residing in the North we find highest regional inequality in March and June/July. We find finer-grained within-region increased steadily from March until July. The importance of spatial context increases over the study period. No analogous pattern is visible for non-COVID mortality. Higher relative deprivation is associated with increased COVID-19 mortality at all stages of the pandemic but does not explain structural inequalities. ConclusionsResults support initial stochastic viral introduction in the South, with initially high inequality decreasing before the establishment of regional trends by June and July, prior to reported regionality of the "second-wave". We outline how this framework can help identify structural factors driving such processes, and offer suggestions for a long-term, locally-targeted model of pandemic relief in tandem with regional support to buffer the social context of the area. Key MessagesO_LIRegional inequality in COVID-19 mortality declined from an initial peak in April, before increasing again in June/July. C_LIO_LIWithin-region inequality increased steadily from March until July. C_LIO_LIStrong regional trends are evident in COVID-19 mortality in June/July, prior to wider reporting of regional differences in "second wave". C_LIO_LIAnalogous spatial inequalities are not present in non-COVID related mortality over the study period. C_LIO_LIThese inequalities are not explained by age structure, care homes, or deprivation. C_LI
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Background: Developing insight into the pathogenesis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is of critical importance to overcome the global pandemic caused by coronavirus disease 2019 (covid-19). In this study, we have applied Mendelian randomization (MR) to systematically evaluate the effect of 10 cardiometabolic risk factors and genetic liability to lifetime smoking on 97 circulating host proteins postulated to either interact or contribute to the maladaptive host response of SARS-CoV-2. Methods: We applied the inverse variance weighted (IVW) approach and several robust MR methods in a two-sample setting to systemically estimate the genetically predicted effect of each risk factor in turn on levels of each circulating protein. Multivariable MR was conducted to simultaneously evaluate the effects of multiple risk factors on the same protein. We also applied MR using cis-regulatory variants at the genomic location responsible for encoding these proteins to estimate whether their circulating levels may influence SARS-CoV-2 severity. Findings: In total, we identified evidence supporting 105 effects between risk factors and circulating proteins which were robust to multiple testing corrections and sensitivity analyses. For example, body mass index provided evidence of an effect on 23 circulating proteins with a variety of functions, such as inflammatory markers c-reactive protein (IVW Beta=0.34 per standard deviation change, 95% CI=0.26 to 0.41, P=2.19x10-16) and interleukin-1 receptor antagonist (IVW Beta=0.23, 95% CI=0.17 to 0.30, P=9.04x10-12). Further analyses using multivariable MR provided evidence that the effect of BMI on lowering immunoglobulin G, an antibody class involved in protecting the body from infection, is substantially mediated by raised triglycerides levels (IVW Beta=-0.18, 95% CI=-0.25 to -0.12, P=2.32x10-08, proportion mediated=44.1%). The strongest evidence that any of the circulating proteins highlighted by our initial analysis influence SARS-CoV-2 severity was identified for soluble glycoprotein 130 (odds ratio=1.81, 95% CI=1.25 to 2.62, P=0.002), a signal transductor for interleukin-6 type cytokines which are involved in the bodys inflammatory response. However, based on current case samples for severe SARS-CoV-2 we were unable to replicate findings in independent samples. Interpretation: Our findings highlight several key proteins which are influenced by established exposures for disease. Future research to determine whether these circulating proteins mediate environmental effects onto risk of SARS-CoV-2 are warranted to help elucidate therapeutic strategies for covid-19 disease severity.
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Recent omic studies prioritised several drug targets associated with coronavirus disease 2019 (COVID-19) severity. However, little evidence was provided to systematically estimate the effect of drug targets on COVID-19 severity in multiple ancestries. In this study, we applied Mendelian randomization (MR) and colocalization approaches to understand the putative causal effects of 16,059 transcripts and 1,608 proteins on COVID-19 severity in European and effects of 610 proteins on COVID-19 severity in African ancestry. We further integrated genetics, clinical and literature evidence to prioritised additional drug targets. Additional sensitivity analyses including multi-trait colocalization and phenome-wide MR were conducted to test for MR assumptions. MR and colocalization prioritized four protein targets, FCRL3, ICAM5, ENTPD5 and OAS1 that showed effect on COVID-19 severity only in European ancestry and one protein target, SERPINA1, only showed effect in African ancestry (odds ratio [OR] in Africans=0.369, 95%CI=0.203 to 0.668, P=9.96x10-4; OR in Europeans=1.021, P=0.745). One protein, ICAM1, showed suggestive effect on COVID-19 severity in both ancestries (OR in Europeans=1.152, 95%CI=1.063 to 1.249, P=5.94x10-4; OR in Africans=1.481, 95%CI=1.008 to 2.176; P=0.045). The phenome-wide MR of the prioritised targets on 622 complex traits identified 726 potential causal effects on other diseases, providing information on potential beneficial and adverse effects. Our study prioritised six proteins as potential drug targets for COVID-19 severity. Several of them were targets of existing drug under trials of COVID-19 or related to the immune system. Most of these targets showed different effects in European and African ancestries, which highlights the value of multi-ancestry MR in informing the generalizability of COVID-19 drug targets across ancestries. This study provides a first step towards clinical investigation on COVID-19 and other types of coronaviruses. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched key terms in PUBMED published before Feb 1st 2022, with the terms: ("COVID-19, "coronavirus") AND ("omics" or "protein" or "transcript") AND ("Genome-wide association study" or "Mendelian randomization"). We found multiple studies identified targeted genes or proteins associated with COVID-19. However, there is little human genetics evidence support the ancestry-consistent or ancestry-specific genes/proteins associated with COVID-19. Added value of this studyTo our knowledge, this is the first comprehensive genetic study that identified protein targets that showed effect on COVID-19 severity in European and African ancestries. Our study identified one protein, SERPINA1, that showed effects on COVID-19 in African ancestry (OR=0.369, P=9.96x10-4), but not in European ancestry (OR=1.021, P=0.745). In addition, our study identified four additional protein targets, FCRL3, ICAM5, ENTPD5 and OAS1, that showed effect on COVID-19 severity in Europeans. One protein ICAM1 showed suggestive effect in both ancestries. Some of these proteins are related to the immune system and/or are targets of existing drug under trials of COVID-19. Implications of all available evidenceOur study prioritised six drug targets for COVID-19 severity, five of them showed different effects in European and African ancestries. This suggested that drug targets may have different responses on COVID-19 severity in different ancestries. Our study also highlights the value of intercellular adhesion molecule (ICAM) family in relation with COVID-19 severity in both ancestries.
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Observational data on COVID-19 including hypothesised risk factors for infection and progression are accruing rapidly, often from non-random sampling such as hospital admissions, targeted testing or voluntary participation. Here, we highlight the challenge of interpreting observational evidence from such samples of the population, which may be affected by collider bias. We illustrate these issues using data from the UK Biobank in which individuals tested for COVID-19 are highly selected for a wide range of genetic, behavioural, cardiovascular, demographic, and anthropometric traits. We discuss the sampling mechanisms that leave aetiological studies of COVID-19 infection and progression particularly susceptible to collider bias. We also describe several tools and strategies that could help mitigate the effects of collider bias in extant studies of COVID-19 and make available a web app for performing sensitivity analyses. While bias due to non-random sampling should be explored in existing studies, the optimal way to mitigate the problem is to use appropriate sampling strategies at the study design stage.
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Alcohol consumption is a serious health issue in Korea in terms of the amount consumed and the behavior related to its consumption. Aldehyde dehydrogenase 2 (ALDH2) is a key enzyme in alcohol metabolism that degrades acetaldehyde to nontoxic acetic acid. The enzyme is coded by the ALDH2 gene, which is commonly polymorphic in East Asian populations. A point mutation in the ALDH2 gene (the rs671 allele) yields an inactive form of ALDH2 that causes acetaldehyde accumulation in the body after alcohol consumption, thereby inhibiting normal alcohol metabolism. Individuals who are homozygous for polymorphism in ALDH2 tend to refrain from drinking alcohol, decreasing their chances of developing alcoholism and exposure to the associated risks. Mendelian randomization (MR) studies have demonstrated that alcohol consumption predicted by ALDH2 genotype is causally related to cardiovascular risks. Moreover, recent MR studies suggest that the ALDH2 variant has mechanistic effects on some disease outcomes or mortality through increased blood levels of acetaldehyde, showing differences therein between heterozygotes (ALDH2*2*2) and homozygotes (ALDH2*1*2) in those who consume alcohol. Accordingly, consideration of ALDH2 genotype in alcohol prevention programs is warranted. In conclusion, strategies that incorporate genetic information and provide an evidential basis from which to help people make informed decisions on alcohol consumption are urgently required.
