Association of COVID-19 with risks of hospitalization and mortality from other disorders post-infection: A study of the UK Biobank (preprint)

ObjectiveTo study whether COVID-19 infection may be associated with increased hospitalization and mortality from other diseases. DesignCohort study. SettingThe UK Biobank. ParticipantsAll subjects in the UK Biobank with available hospitalization records and alive as of 31-Jan-2020 (N= 412,096; age 50-87). Main outcome measuresWe investigated associations of COVID-19 with hospitalization and mortality due to different diseases post-infection. We conducted a comprehensive survey on disorders from all systems (up to 135 disease categories). Multivariable Cox and Poisson regression was conducted controlling for main confounders. For sensitivity analysis, we also conducted separate analysis for new-onset and recurrent cases, and other analysis such as the prior event rate adjustment(PERR) approach to minimize effects of unmeasured confounders. We also performed association analyses stratified by vaccination status. Time-dependent effects on subsequent hospitalization and mortality were also tested. ResultsCompared to individuals with no known history of COVID-19, those with severe COVID-19 (requiring hospitalization) exhibited higher hazards of hospitalization and/or mortality due to multiple disorders (median follow-up=608 days), including disorders of respiratory, cardiovascular, neurological, gastrointestinal, genitourinary and musculoskeletal systems. Increased hazards of hospitalizations and/or mortality were also observed for injuries due to fractures, various infections and other non-specific symptoms. These results remained largely consistent after sensitivity analyses. Severe COVID-19 was also associated with increased all-cause mortality (HR=14.700, 95% CI: 13.835-15.619). Mild (non-hospitalized) COVID-19 was associated with modestly increased risk of all-cause mortality (HR=1.237, 95% CI 1.037-1.476) and mortality from neurocognitive disorders, as well as hospital admission from a few disorders such as aspiration pneumonitis, musculoskeletal pain and other general signs/symptoms. All-cause mortalities and hospitalizations from other disorders post-infection were generally higher in the pre-vaccination era. The deleterious effect of COVID-19 was observed to wane over time, with maximum HR in the initial phase. ConclusionsIn conclusion, this study revealed increased risk of hospitalization and mortality from a wide variety of pulmonary and extra-pulmonary diseases after COVID-19, especially for severe infections. Mild disease was also associated with increased all-cause mortality. Causality however cannot be established due to observational nature of the study. Further studies are required to replicate our findings.

Association of COVID-19 vaccination with risks of hospitalization and mortality due to cardiovascular and other diseases: A study of the UK Biobank (preprint)

BackgroundVaccines for COVID-19 represent a breakthrough in the fight against the pandemic. However, worries about adverse effects have led to vaccine hesitancy in some people. On the other hand, as COVID-19 may be associated with a range of sequelae, it is possible that vaccines may protect against hospitalization and mortality of various diseases. MethodsWe leveraged a large prospective cohort, the UK-Biobank (UKBB), and studied associations of at least one dose of COVID-19 vaccination (BioNTech BNT162b2 or Oxford-AstraZeneca ChAdOx1 nCoV-19) with hospitalization and mortalities from cardiovascular and other diseases (N=180,727). Multivariable Cox and Poisson regression was conducted controlling for main confounders. For hospitalizations, we also conducted separate analysis for new-onset and recurrent cases. All-cause and cardiovascular mortality were also included as outcome. ResultsWe observed that COVID-19 vaccination (at least one dose) was associated with lower risks of hospitalizations from stroke (hazard ratio [HR]=0.371, 95% CI: 0.254-0.543, p=3.36e-7), venous thromboembolism (VTE) (HR=0.485, 95% CI: 0.292-0.804, p=4.99e-3), dementia (HR=0.207, 95% CI 0.091-0.470; p=1.66e-4) and non-COVID-19 pneumonia (HR=0.482, 95% CI 0.313-0.742; p=9.18e-4). Regarding mortality, an association with lower all-cause and cardiovascular mortality was observed, as well as lower mortality from several diseases including stroke, coronary artery disease(CAD), and chronic obstructive pulmonary disease (COPD) and dementia. There is no evidence that vaccination was associated with increased hospitalization/fatality from any specific disorders. ConclusionsTaken together, this study provides further support to the safety and benefits of COVID-19 vaccination, and such benefits may extend beyond reduction of infection risk or severity per se. As an observational study, causal relationship cannot be concluded and further studies are required to verify the findings.

Neuropsychiatric disorders as risk factors and consequences of COVID-19: A Mendelian randomization study (preprint)

Background More than 170 million cases of COVID-19 have been reported worldwide. It has been proposed that psychiatric disorders may be risk factors and/or consequences of COVID-19 infection. However, observational studies could be affected by confounding bias. Methods We performed bi-directional two-sample Mendelian randomization (MR) analysis to evaluate causal relationships between liability to COVID-19 (and severe/critical infection) and a wide range of neuropsychiatric disorders or traits. We employed the latest GWAS summary statistics from the COVID-19 Host Genetics Initiative. A variety of MR methods including those accounting for horizontal pleiotropy were used. Results Overall we observed evidence that liability to COVID-19 or severe infection may be causally associated with higher risks of post-traumatic stress disorder (PTSD), bipolar disorder (BD) (especially BD II), schizophrenia (SCZ), attention deficit hyperactivity disorder (ADHD) and suicidal thought (ST) when compared to the general population. On the other hand, liability to a few psychiatric traits/disorders, for example ADHD, alcohol and opioid use disorders may be causally associated with higher risks of COVID-19 infection or severe disease. In genetic correlation analysis, cannabis use disorder, ADHD, and anxiety showed significant and positive genetic correlation with critical or hospitalized infection. All the above findings passed multiple testing correction at a false discovery rate (FDR)<0.05. For pneumonia, in general we observed a different pattern of associations, with bi-directional positive associations with depression- and anxiety-related phenotypes. Conclusions In summary, this study provides evidence for tentative bi-directional causal associations between liability to COVID-19 (and severe infection) and a number of neuropsychiatric disorders. Further replications and prospective studies are required to verify the findings.

Exploring causal relationships between COVID-19 and cardiometabolic disorders: A bi-directional Mendelian randomization study (preprint)

Background: More than 100 million cases of COVID-19 have been reported worldwide. A number of risk factors for infection or severe infection have been identified, however observational studies were subject to confounding bias. In addition, there is still limited knowledge about the complications or medical consequences of the disease. Methods: Here we performed bi-directional Mendelian randomization (MR) analysis to evaluate causal relationships between liability to COVID-19 (and severe/critical infection) and a wide range of around 30 cardiometabolic disorders (CMD) or traits. Genetic correlation (rg) was assessed by LD score regression(LDSC). The latest GWAS summary statistics from the COVID-19 Host Genetics Initiative was used, which comprised comparisons of general population controls with critically ill, hospitalized and any infected cases. Results: Overall we observed evidence that liability to COVID-19 or severe infection may be causally associated with higher risks of type 2 diabetes mellitus(T2DM), chronic kidney disease(CKD), ischemic stroke (especially large artery stroke[LAS]) and heart failure(HF) when compared to the general population. On the other hand, our findings suggested that liability to atrial fibrillation (AF), stroke (especially LAS), obesity, diabetes (T1DM and T2DM), low insulin sensitivity and impaired renal function (low eGFR and diabetic kidney disease) may be causal risk factors for COVID-19 or severe disease. In genetic correlation analysis, T2DM, CAD, obesity, fasting insulin, CKD, gout, stroke and urate showed positive rg with critical or hospitalized infection. All above findings passed multiple testing correction at a false discovery rate (FDR)<0.05. Conclusions: In summary, this study provides evidence for tentative bi-directional causal associations between liability to COVID-19 and severe disease and a number of CM disorders. Further replications and prospective studies are required to verify the findings.

Exploring drugs and vaccines associated with altered risks and severity of COVID-19: a UK Biobank cohort study of all ATC level-4 drug categories (preprint)

BackgroundCOVID-19 is a major public health concern, yet its risk factors are not well-understood and effective therapies are lacking. It remains unclear how different drugs may increase or decrease the risks of infection and severity of disease. MethodsWe studied associations of prior use of all level-4 ATC drug categories (including vaccines) with COVID-19 diagnosis and outcome, based on a prospective cohort of UK Biobank(UKBB). Drug history was based on general practitioner(GP) records. Effects of prescribed medications/vaccinations on the risk of infection, severity of disease and mortality were investigated separately. Hospitalized and fatal cases were categorized as severe infection. We also considered different study designs and conducted analyses within infected patients, tested subjects and the whole population respectively, and for 5 different time-windows of prescriptions. Missing data were accounted for by multiple imputation and inverse probability weighting was employed to reduce testing bias. Multivariable logistic regression was conducted which controls for main confounders. ResultsWe placed a greater focus on protective associations here, as (residual) confounding by indication and comorbidities tends to bias towards harmful effects. Across all categories, statins showed the strongest and most consistent protective associations. Significant protective effects against severe infection were seen among infected subjects (OR for prescriptions within a 12-month window, same below: 0.50, 95% CI:0.42-0.60), tested subjects (OR=0.63, 0.54-0.73) or in the general population (OR=0.49, 0.42-0.57). A number of top-listed drugs with protective effects were also cardiovascular medications, such as angiotensin converting enzyme inhibitors, angiotensin receptor blockers, calcium channel blocker and beta-blockers. Some other drugs showing protective associations included biguanides (metformin), estrogens, thyroid hormones and proton pump inhibitors, among others. Interestingly, we also observed protective associations by numerous vaccines. The most consistent association was observed for influenza vaccines, which showed reduced odds of infection (OR= 0.73 for vaccination in past year, CI 0.65-0.83) when compared cases to general population controls or test-negative controls (OR=0.60, 0.53-0.68). Protective associations were also observed when severe or fatal infection was considered as the outcome. Pneumococcal, tetanus, typhoid and combined bacterial and viral vaccines (ATC code J07CA) were also associated with lower odds of infection/severity. Further subgroup and interaction analyses revealed difference in protective effects in different clinical subgroups. For example, protective effects of flu and pneumococcal vaccines were weaker in obese individuals, while we observed stronger protective effects of statins in those with cardiometabolic disorders, such as diabetes, coronary artery disease, hypertension and obesity. ConclusionsA number of drugs, including many for cardiometabolic disorders, may be associated with lower odds of infection/severity of infection. Several existing vaccines, especially flu vaccines, may be beneficial against COVID-19 as well. However, causal relationship cannot be established due to risk of confounding. While further studies are required to validate the findings, this work provides a useful reference for future meta-analyses, clinical trials or experimental studies.

Uncovering clinical risk factors and prediction of severe COVID-19: A machine learning approach based on UK Biobank data (preprint)

Background: COVID-19 is a major public health concern. Given the extent of the pandemic, it is urgent to identify risk factors associated with severe disease. Accurate prediction of those at risk of developing severe infections is also important clinically. Methods: Based on the UK Biobank (UKBB data), we built machine learning(ML) models to predict the risk of developing severe or fatal infections, and to evaluate the major risk factors involved. We first restricted the analysis to infected subjects, then performed analysis at a population level, considering those with no known infections as controls. Hospitalization was used as a proxy for severity. Totally 93 clinical variables (collected prior to the COVID-19 outbreak) covering demographic variables, comorbidities, blood measurements (e.g. hematological/liver and renal function/metabolic parameters etc.), anthropometric measures and other risk factors (e.g. smoking/drinking habits) were included as predictors. XGboost (gradient boosted trees) was used for prediction and predictive performance was assessed by cross-validation. Variable importance was quantified by Shapley values and accuracy gain. Shapley dependency and interaction plots were used to evaluate the pattern of relationship between risk factors and outcomes. Results: A total of 1191 severe and 358 fatal cases were identified. For the analysis among infected individuals (N=1747), our prediction model achieved AUCs of 0.668 and 0.712 for severe and fatal infections respectively. Since only pre-diagnostic clinical data were available, the main objective of this analysis was to identify baseline risk factors. The top five contributing factors for severity were age, waist-hip ratio(WHR), HbA1c, number of drugs taken(cnt_tx) and gamma-glutamyl transferase levels. For prediction of mortality, the top features were age, systolic blood pressure, waist circumference (WC), urea and WHR. In subsequent analyses involving the whole UKBB population (N for controls=489987), the corresponding AUCs for severity and fatality were 0.669 and 0.749. The same top five risk factors were identified for both outcomes, namely age, cnt_tx, WC, WHR and cystatin C. We also uncovered other features of potential relevance, including testosterone, IGF-1 levels, red cell distribution width (RDW) and lymphocyte percentage. Conclusions: We identified a number of baseline clinical risk factors for severe/fatal infection by an ML approach. For example, age, central obesity, impaired renal function, multi-comorbidities and cardiometabolic abnormalities may predispose to poorer outcomes. The presented prediction models may be useful at a population level to help identify those susceptible to developing severe/fatal infections, hence facilitating targeted prevention strategies. Further replications in independent cohorts are required to verify our findings.

Exploring diseases/traits and blood proteins causally related to expression of ACE2, the putative receptor of 2019-nCov: A Mendelian Randomization analysis (preprint)

COVID-19 has become a major public health problem globally. There is good evidence that ACE2 is a receptor for SARS-CoV-2, and high expression of ACE2 may increase susceptibility to infection. We aimed to explore risk factors affecting susceptibility to infection and prioritize drug repositioning candidates, based on Mendelian randomization (MR) studies on ACE2 lung expression. We conducted a phenome-wide MR study to prioritize diseases/traits and blood proteins causally linked to ACE2 lung expression in GTEx. We also explored drug candidates whose targets overlapped with the top-ranked proteins in MR, as these drugs may alter ACE2 expression and may be clinically relevant. The most consistent finding was tentative evidence of an association between diabetes-related traits and increased ACE2 expression. Based on one of the largest GWAS on type 2 diabetes (T2DM) to date (N=898,130), T2DM was causally linked to raised ACE2 expression (p=2.91E-03;MR-IVW). Significant associations (at nominal level; p<0.05) with ACE2 expression was observed across multiple DM datasets and analytic methods, for type 1 and 2 diabetes and related traits including early start of insulin. Other diseases/traits having nominal significant associations with increased expression included inflammatory bowel disease, (ER+) breast and lung cancers, asthma, smoking and elevated ALT. We also identified drugs that may target the top-ranked proteins in MR, such as fostamatinib and zinc. In conclusion, our analysis suggested that diabetes and related traits may increase ACE2 expression, which may influence susceptibility to infection (or more severe infection). However, none of these findings withstood rigorous multiple testing corrections (at FDR<0.05). Proteome-wide MR analyses might help uncover mechanisms underlying ACE2 expression and guide drug repositioning. Further studies are required to verify our findings.