Cardiometabolic Traits, Sepsis, and Severe COVID-19: A Mendelian Randomization Investigation

Causal relationships between interleukins, interferons and COVID-19 risk: a Mendelian randomization study

Observational studies have shown close associations between COVID-19 risk and cytokines, especially interleukins (ILs) and interferons (IFNs). However, the causal relationships between ILs, IFNs and COVID-19 were still unclear. To resolve the problem, we conducted a Mendelian randomization analysis between COVID-19 and 47 cytokines, including 35 ILs and 12 IFNs. First, three methods were applied to estimate causal effects by using single nucleotide polymorphisms as instrumental variables (IVs). Subsequently, the MR-Egger method was used to estimate the horizontal pleiotropy of IVs. Finally, sensitivity analyses were applied to assess the robustness of results. As a result, one IFN (IFN-W1) and five ILs (IL-5, IL-6, IL-13, IL-16 and IL-37) were identified to significantly decrease the COVID-19 risk. In contrast, one IFN (IFNG) and five ILs (IL-3, IL-8, IL-27, IL-31 and IL-36β) were found to be significantly associated with an increased risk of COVID-19. In summary, the findings of this study provide insights into potential therapeutic interventions for COVID-19.

Using Mendelian randomization provides genetic insights into potential targets for sepsis treatment

Sepsis is recognized as a major contributor to the global disease burden, but there is a lack of specific and effective therapeutic agents. Utilizing Mendelian randomization (MR) methods alongside evidence of causal genetics presents a chance to discover novel targets for therapeutic intervention. MR approach was employed to investigate potential drug targets for sepsis. Pooled statistics from IEU-B-4980 comprising 11,643 cases and 474,841 controls were initially utilized, and the findings were subsequently replicated in the IEU-B-69 (10,154 cases and 454,764 controls). Causal associations were then validated through colocalization. Furthermore, a range of sensitivity analyses, including MR-Egger intercept tests and Cochran's Q tests, were conducted to evaluate the outcomes of the MR analyses. Three drug targets (PSMA4, IFNAR2, and LY9) exhibited noteworthy MR outcomes in two separate datasets. Notably, PSMA4 demonstrated not only an elevated susceptibility to sepsis (OR 1.32, 95% CI 1.20-1.45, p = 1.66E-08) but also exhibited a robust colocalization with sepsis (PPH4 = 0.74). According to the present MR analysis, PSMA4 emerges as a highly encouraging pharmaceutical target for addressing sepsis. Suppression of PSMA4 could potentially decrease the likelihood of sepsis.

Causal relationship between telomere length and sepsis: a bidirectional Mendelian randomization study

Numerous observational studies have elucidated a connection between leukocyte telomere length (LTL) and sepsis, yet its fundamental cause remains enigmatic. Thus, the current study's objective is to employ a bidirectional Mendelian randomization (MR) approach to scrutinize the causality between LTL and sepsis. We selected single nucleotide polymorphisms (SNPs) associated with LTL (n = 472,174) and sepsis from a genome-wide association study (GWAS), including Sepsis (n = 486,484, ncase = 11,643), Sepsis (28 day death in critical care) (n = 431,365, ncase = 347), Sepsis (under 75) (n = 462,869, ncase = 11,568), Sepsis (28 day death) (n = 486,484, ncase = 1896), and Sepsis (critical care) (n = 431,365, ncase = 1380), as instrumental variables (IVs). The inverse variance weighted (IVW) MR method was employed as the primary approach, and various sensitivity analyses were conducted to assess the validity of this instrument and potential pleiotropy. Using the IVW method, we uncovered a potential causal relationship between genetically predicted LTL reduction and increased susceptibility to sepsis, with an odds ratio (OR) of 1.161 [95% confidence interval (CI) 1.039-1.297, p = 0.008]. However, reverse MR analysis did not indicate any impact of sepsis on LTL. Our forward MR study highlights a potential causal relationship between LTL as an exposure and increased susceptibility to sepsis. Specifically, our findings suggest that individuals with genetically determined shorter LTL may be at an increased risk of developing sepsis. This may contribute to the development of novel diagnostic and therapeutic strategies for the prevention, diagnosis, and treatment of sepsis.

Associations of educational attainment with Sepsis mediated by metabolism traits and smoking: a Mendelian randomization study

Objective

Sepsis constitutes a significant global healthcare burden. Studies suggest a correlation between educational attainment and the likelihood of developing sepsis. Our goal was to utilize Mendelian randomization (MR) in order to examine the causal connection between educational achievement (EA) and sepsis, while measuring the mediating impacts of adjustable variables.

Methods

We collected statistical data summarizing educational achievement (EA), mediators, and sepsis from genome-wide association studies (GWAS). Employing a two-sample Mendelian randomization (MR) approach, we calculated the causal impact of education on sepsis. Following this, we performed multivariable MR analyses to assess the mediation proportions of various mediators, including body mass index (BMI), smoking, omega-3 fatty acids, and apolipoprotein A-I(ApoA-I).

Results

Genetic prediction of 1-SD (4.2 years) increase in educational attainment (EA) was negatively correlated with sepsis risk (OR = 0.83, 95% CI 0.71 to 0.96). Among the four identified mediators, ranked proportionally, they including BMI (38.8%), smoking (36.5%), ApoA-I (6.3%) and omega-3 (3.7%). These findings remained robust across a variety of sensitivity analyses.

Conclusion

The findings of this study provided evidence for the potential preventive impact of EA on sepsis, which may be influenced by factors including and metabolic traits and smoking. Enhancing interventions targeting these factors may contribute to reducing the burden of sepsis.

The causal effects of circulating cytokines on sepsis: a Mendelian randomization study

Background

In observational studies, sepsis and circulating levels of cytokines have been associated with unclear causality. This study used Mendelian randomization (MR) to identify the causal direction between circulating cytokines and sepsis in a two-sample study.

Methods

An MR analysis was performed to estimate the causal effect of 41 cytokines on sepsis risk. The inverse-variance weighted random-effects method, the weighted median-based method, and MR-Egger were used to analyze the data. Heterogeneity and pleiotropy were assessed using MR-Egger regression and Cochran's Q statistic.

Results

Genetically predicted beta-nerve growth factor (OR = 1.12, 95% CI [1.037-1.211], P = 0.004) increased the risk of sepsis, while RANTES (OR = 0.92, 95% CI [0.849-0.997], P = 0.041) and fibroblast growth factor (OR = 0.869, 95% CI [0.766-0.986], P = 0.029) reduced the risk of sepsis. These findings were robust in extensive sensitivity analyses. There was no clear association between the other cytokines and sepsis risk.

Conclusion

The findings of this study demonstrate that beta-nerve growth factor, RANTES, and fibroblast growth factor contribute to sepsis risk. Investigations into potential mechanisms are warranted.

Investigating the impact of human blood metabolites on the Sepsis development and progression: a study utilizing two-sample Mendelian randomization

Background

Existing data suggests a potential link between human blood metabolites and sepsis, yet the precise cause-and-effect relationship remains elusive. By using a two-sample Mendelian randomization (MR) analysis, this study aims to establish a causal link between human blood metabolites and sepsis.

Methods

A two-sample MR analysis was employed to investigate the relationship between blood metabolites and sepsis. To assess the causal connection between sepsis and human blood metabolites, five different MR methods were employed, A variety of sensitivity analyses were conducted, including Cochrane's Q test, MR-Egger intercept test, MR-PRESSO and leave-one-out (LOO) analysis. In order to ensure the robustness of the causal association between exposure and outcome, the Bonferroni adjustment was employed. Additionally, we conducted analyses of the metabolic pathways of the identified metabolites using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and the Small Molecule Pathway Database (SMPDB) database.

Results

The MR analysis revealed a total of 27 metabolites (16 known and 11 unknown) causally linked to the development and progression of sepsis. After applying the Bonferroni correction, 3-carboxy-4-methyl-5-propyl-2-furanpropanoate (CMPF) remained significant in relation to 28-day all-cause mortality in sepsis. By pathway enrichment analysis, we identified four significant metabolic pathways. Notably, the Alpha Linolenic Acid and Linoleic Acid metabolism pathway emerged as a pivotal contributor to the occurrence and progression of sepsis.

Conclusion

This study provides preliminary evidence of causal associations between human blood metabolites and sepsis, as ascertained by MR analysis. The findings offer valuable insights into the pathogenesis of sepsis and may provide insight into preventive and therapeutic approaches.

Identification of genetic loci jointly influencing COVID-19 and coronary heart diseases

BACKGROUND

Comorbidities of coronavirus disease 2019 (COVID-19)/coronary heart disease (CHD) pose great threats to disease outcomes, yet little is known about their shared pathology. The study aimed to examine whether comorbidities of COVID-19/CHD involved shared genetic pathology, as well as to clarify the shared genetic variants predisposing risks common to COVID-19 severity and CHD risks.

METHODS

By leveraging publicly available summary statistics, we assessed the genetically determined causality between COVID-19 and CHD with bidirectional Mendelian randomization. To further quantify the causality contributed by shared genetic variants, we interrogated their genetic correlation with the linkage disequilibrium score regression method. Bayesian colocalization analysis coupled with conditional/conjunctional false discovery rate analysis was applied to decipher the shared causal single nucleotide polymorphisms (SNPs).

FINDINGS

Briefly, we observed that the incident CHD risks post COVID-19 infection were partially determined by shared genetic variants. The shared genetic variants contributed to the causality at a proportion of 0.18 (95% CI 0.18-0.19) to 0.23 (95% CI 0.23-0.24). The SNP (rs10490770) located near LZTFL1 suggested direct causality (SNPs → COVID-19 → CHD), and SNPs in ABO (rs579459, rs495828), ILRUN(rs2744961), and CACFD1(rs4962153, rs3094379) may simultaneously influence COVID-19 severity and CHD risks.

INTERPRETATION

Five SNPs located near LZTFL1 (rs10490770), ABO (rs579459, rs495828), ILRUN (rs2744961), and CACFD1 (rs4962153, rs3094379) may simultaneously influence their risks. The current study suggested that there may be shared mechanisms predisposing to both COVID-19 severity and CHD risks. Genetic predisposition to COVID-19 is a causal risk factor for CHD, supporting that reducing the COVID-19 infection risk or alleviating COVID-19 severity among those with specific genotypes might reduce their subsequent CHD adverse outcomes. Meanwhile, the shared genetic variants identified may be of clinical implications for identifying the target population who are more vulnerable to adverse CHD outcomes post COVID-19 and may also advance treatments of 'Long COVID-19.'

Association between COVID-19 and myasthenia gravis (MG): A genetic correlation and Mendelian randomization study

BACKGROUND

Observational studies have suggested an association between coronavirus disease 2019 (COVID-19) and myasthenia gravis (MG). Here, we aimed to estimate the genetic correlation and causal relationship between COVID-19 susceptibility, hospitalization, severity, and MG phenotypes using linkage disequilibrium score regression (LDSC) and Mendelian randomization (MR) approach.

METHODS

Summary statistics of COVID-19 susceptibility, hospitalization, and severity were used as instrumental variables for exposure traits. Large-scale genome-wide association study (GWAS) data for MG were used as outcome traits. The inverse variance weighted approach was used for the main MR analysis, complemented by MR-Egger, weighted median, simple mode, and weighted mode methods. Sensitivity analysis was implemented using Cochran's Q test, MR-PRESSO method, and MR-Egger intercept test.

RESULTS

LDSC analysis did not reveal any genetic correlation among COVID-19 susceptibility, hospitalization, severity, and MG phenotypes, including MG, early-onset MG, and late-onset MG (p > .05). Our MR analysis did not provide evidence supporting a causal effect of COVID-19 susceptibility, hospitalization, or severity on MG phenotypes (p > .05). Extensive sensitivity analysis strengthened the robustness and consistency of the MR estimates.

CONCLUSION

Our study did not find evidence of a genetic correlation or causal relationship among COVID-19 susceptibility, hospitalization, severity, and MG. Future studies with more GWAS data are needed to evaluate the association between COVID-19 phenotypes and MG and its subgroups.

Association of Genetically Predicted Insomnia With Risk of Sepsis: A Mendelian Randomization Study

Importance

Insomnia has been associated with altered inflammatory response as well as increased risk of infections and sepsis in observational studies. However, these studies are prone to bias, such as residual confounding. To further understand the potential causal association between insomnia and sepsis risk, a 2-sample Mendelian randomization (MR) approach should be explored.

Objective

To evaluate whether genetically predicted insomnia is associated with risk of sepsis.

Design, Setting, and Participants

Two-sample MR was performed to estimate the association between genetically predicted insomnia and sepsis risk. Data were obtained from a genome-wide association study identifying 555 independent genetic variants (R2 < 0.01) strongly associated with insomnia (P < 5 × 10-8). Sensitivity analyses were conducted to address bias due to pleiotropy and sample overlap, along with mediation analyses and sex-stratified analyses. The insomnia data set included 2.4 million individuals of European ancestry from the UK Biobank and 23andMe. For sepsis, 462 918 individuals of European ancestry from the UK Biobank were included. Data were extracted between February and December 2022 and analyzed between March 2022 and March 2023.

Exposure

Genetically predicted insomnia.

Main Outcome and Measure

Sepsis.

Results

There were 593 724 individuals with insomnia and 10 154 cases of sepsis. A doubling in the population prevalence of genetically predicted insomnia was associated with an odds ratio of 1.37 (95% CI, 1.19-1.57; P = 7.6 × 10-6) for sepsis. Sensitivity analyses supported this observation. One-third of the association between genetically predicted insomnia and risk of sepsis was mediated through a combination of cardiometabolic risk factors for sepsis (body mass index, type 2 diabetes, smoking, or cardiovascular disease; overall proportion, 35.2%; 95% CI, 5.1-76.9). The association between insomnia and sepsis was more pronounced among women compared with men (women: odds ratio, 1.44; 95% CI, 1.24-1.68; men: OR, 1.10; 95% CI, 0.86-1.40).

Conclusions and Relevance

The concordance between these findings and previous observational studies supports that insomnia is potentially causally associated with the risk of sepsis. Thus, insomnia is a potential preventable risk factor of sepsis that should be further investigated, also in non-European populations.

Are Patients With an International Classification of Diseases, 10th Edition Discharge Diagnosis Code for Sepsis Different in Regard to Demographics and Outcome Variables When Comparing Those With Sepsis Only to Those Also Diagnosed With COVID-19 or Those With a COVID-19 Diagnosis Alone?

OBJECTIVES

We analyzed whether patients with the International Classification of Diseases, 10th Edition (ICD-10) discharge diagnosis code for sepsis are different in regard to demographics and outcome variables when comparing those with sepsis only to those also diagnosed with COVID-19 or those with a COVID-19 diagnosis alone.

DESIGN

Retrospective cohort study.

SETTING

Nine hospitals in an academic health system.

PATIENTS

Patients with a final ICD-10 discharge diagnostic code for sepsis only, a diagnosis of COVID-19-only, or a final sepsis ICD-10 discharge code + a diagnosis of COVID-19 admitted to the hospital were analyzed for demographic and outcome differences between the cohorts.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

A total of 11,395 patients met inclusion criteria: 6,945 patients (60.9%) were ICD-10 sepsis code only, 3,294 patients (28.9%) were COVID-19 diagnosis-only, and 1,153 patients (10.1%) were sepsis ICD-10 code + COVID-19 diagnosis. Comparing sepsis ICD-10 code + COVID-19 diagnosis patients to sepsis ICD-10 code only and COVID-19 diagnosis-only patients, the sepsis ICD-10 code + COVID-19 diagnosis patients were: older (69 [58-78] vs 67 [56-77] vs 64 [51-76] yr), less likely to be female (40.3% vs 46.7% vs 49.5%), more frequently admitted to the ICU (59.3% [684/1,153] vs 54.9% [1,810/3,297] vs 15% [1,042/6,945]), more frequently required ventilatory support (39.3% [453/1,153] vs 31.8% [1,049/3,297] vs 6.0% [417/6,945]), had longer median hospital length of stay (9 [5,16] vs 5 [3,8] vs 7. [4,13] d), and were more likely to die in the hospital (39.2% [452/1,153] vs 22.3% [735/3,297] vs 6.4% [444/6,945]).

CONCLUSIONS

During the COVID-19 pandemic the sickest cohort of patients was those receiving an explicit ICD-10 code of sepsis + a COVID-19 diagnosis. A significant percentage of COVID-19 diagnosis-only patients appear to have been under-coded as they received a level of critical care (ICU admission; intubation) suggestive of the presence of acute organ dysfunction during their admission.

A novel penalized inverse-variance weighted estimator for Mendelian randomization with applications to COVID-19 outcomes

Mendelian randomization utilizes genetic variants as instrumental variables (IVs) to estimate the causal effect of an exposure variable on an outcome of interest even in the presence of unmeasured confounders. However, the popular inverse-variance weighted (IVW) estimator could be biased in the presence of weak IVs, a common challenge in MR studies. In this article, we develop a novel penalized inverse-variance weighted (pIVW) estimator, which adjusts the original IVW estimator to account for the weak IV issue by using a penalization approach to prevent the denominator of the pIVW estimator from being close to zero. Moreover, we adjust the variance estimation of the pIVW estimator to account for the presence of balanced horizontal pleiotropy. We show that the recently proposed debiased IVW (dIVW) estimator is a special case of our proposed pIVW estimator. We further prove that the pIVW estimator has smaller bias and variance than the dIVW estimator under some regularity conditions. We also conduct extensive simulation studies to demonstrate the performance of the proposed pIVW estimator. Furthermore, we apply the pIVW estimator to estimate the causal effects of five obesity-related exposures on three coronavirus disease 2019 (COVID-19) outcomes. Notably, we find that hypertensive disease is associated with an increased risk of hospitalized COVID-19; and peripheral vascular disease and higher body mass index are associated with increased risks of COVID-19 infection, hospitalized COVID-19, and critically ill COVID-19.

Gut microbiota and sepsis: bidirectional Mendelian study and mediation analysis

Background

There is a growing body of evidence that suggests a connection between the composition of gut microbiota and sepsis. However, more research is needed to better understand the causal relationship between the two. To gain a deeper insight into the association between gut microbiota, C-reactive protein (CRP), and sepsis, we conducted several Mendelian randomization (MR) analyses.

Methods

In this study, publicly available genome-wide association study (GWAS) summary statistics were examined to determine the correlation between gut microbiota and sepsis, including various sepsis subgroups (such as under 75, 28-day death, Critical Care Units (ICU), 28-day death in ICU). Initially, two-sample and reverse Mendelian randomization (MR) analyses were conducted to identify causality between gut microbiota and sepsis. Subsequently, multivariable and two-step MR analyses revealed that the relationship between microbiota and sepsis was mediated by CRP. The robustness of the findings was confirmed through several sensitivity analyses.

Findings

In our study, we revealed positive correlations between 24 taxa and different sepsis outcomes, while 30 taxa demonstrated negative correlations with sepsis outcomes. Following the correction for multiple testing, we found that the Phylum Lentisphaerae (OR: 0.932, p = 2.64E-03), class Lentisphaeria, and order Victivallales (OR: 0.927, p = 1.42E-03) displayed a negative relationship with sepsis risk. In contrast, Phylum Tenericutes and class Mollicutes (OR: 1.274, p = 2.89E-03) were positively related to sepsis risk and death within 28 days. It is notable that Phylum Tenericutes and class Mollicutes (OR: 1.108, p = 1.72E-03) also indicated a positive relationship with sepsis risk in individuals under 75. From our analysis, it was shown that C-reactive protein (CRP) mediated 32.16% of the causal pathway from Phylum Tenericutes and class Mollicutes to sepsis for individuals under 75. Additionally, CRP was found to mediate 31.53% of the effect of the genus Gordonibacter on sepsis. Despite these findings, our reverse analysis did not indicate any influence of sepsis on the gut microbiota and CRP levels.

Conclusion

The study showcased the connection between gut microbiota, CRP, and sepsis, which sheds new light on the potential role of CRP as a mediator in facilitating the impact of gut microbiota on sepsis.

Genetic association of lipids and lipid-lowering drugs with sepsis: a Mendelian randomization and mediation analysis

Background

The impact of lipid-lowering medications on sepsis is still not well defined. A Mendelian randomization (MR) study was carried out to probe the causal connections between genetically determined lipids, lipid-reducing drugs, and the risk of sepsis.

Materials and methods

Data on total serum cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoprotein A-I (ApoA-I), apolipoprotein B (ApoB), and triglycerides (TG) were retrieved from the MR-Base platform and the Global Lipids Genetics Consortium in 2021 (GLGC2021). Our study categorized sepsis into two groups: total sepsis and 28-day mortality of sepsis patients (sepsis28). The inverse-variance weighted (IVW) method was the primary method used in MR analysis. Cochran's Q test and the MR-Egger intercept method were used to assess the heterogeneity and pleiotropy.

Results

In the MR analysis, we found that ApoA-I played a suggestively positive role in protecting against both total sepsis (OR, 0.863 per SD increase in ApoA-I; 95% CI, 0.780-0.955; P = 0.004) and sepsis28 (OR, 0.759; 95% CI, 0.598-0.963; P = 0.023). HDL-C levels were also found to suggestively reduce the incidence of total sepsis (OR, 0.891 per SD increase in HDL-C; 95% CI, 0.802-0.990; P = 0.031). Reverse-MR showed that sepsis28 led to a decrease in HDL-C level and an increase in TG level. In drug-target MR, we found that HMGCR inhibitors positively protected against total sepsis (1 OR , 0.719 per SD reduction in LDL-C; 95% CI, 0.540-0.958; P = 0.024). LDL-C and HDL-C proxied CETP inhibitors were found to have a protective effect on total sepsis, with only LDL-C proxied CETP inhibitors showing a suggestively protective effect on sepsis28. In Mediated-MR, BMI exhibited a negative indirect effect in HMGCR inhibitors curing sepsis. The indirect impact of ApoA-I explained over 50% of the curative effects of CETP inhibitors in sepsis.

Conclusions

Our MR study suggested that ApoA-I and HDL-C protected against sepsis, while HMGCR and CETP inhibitors showed therapeutic potential beyond lipid-lowering effects. ApoA-I explained the effects of CETP inhibitors. Our study illuminates how lipids affect sepsis patients and the effectiveness of new drugs, opening new avenues for sepsis treatment.

Gestational age at birth and hospitalisations for infections among individuals aged 0-50 years in Norway: a longitudinal, register-based, cohort study

Background

Preterm birth is associated with increased risk of childhood infections. Whether this risk persists into adulthood is unknown and limited information is available on risk patterns across the full range of gestational ages.

Methods

In this longitudinal, register-based, cohort study, we linked individual-level data on all individuals born in Norway (January 01, 1967-December 31, 2016) to nationwide hospital data (January 01, 2008-December 31, 2017). Gestational age was categorised as 23-27, 28-31, 32-33, 34-36, 37-38, 39-41, and 42-44 completed weeks. The analyses were stratified by age at follow-up: 0-11 months and 1-5, 6-14, 15-29, and 30-50 years. The primary outcome was hospitalisation due to any infectious disease, with major infectious disease groups as secondary outcomes. Adjusted hospitalisation rate ratios (RRs) for any infection and infectious disease groups were estimated using negative binomial regression. Models were adjusted for year of birth, maternal age at birth, parity, and sex, and included an offset parameter adjusted for person-time at risk.

Findings

Among 2,695,830 individuals with 313,940 hospitalisations for infections, we found a pattern of higher hospitalisation risk in lower gestational age groups, which was the strongest in childhood but still evident in adulthood. Comparing those born very preterm (28-31) and late preterm (34-36) to full-term (39-41 weeks), RRs (95% confidence interval) for hospitalisation for any infectious disease at ages 1-5 were 3.3 (3.0-3.7) and 1.7 (1.6-1.8), respectively. At 30-50 years, the corresponding estimates were 1.4 (1.2-1.7) and 1.2 (1.1-1.3). The patterns were similar for the infectious disease groups, including bacterial and viral infections, respiratory tract infections (RTIs), and infections not attributable to RTIs.

Interpretation

Increasing risk of hospitalisations for infections in lower gestational age groups was most prominent in children but still evident in adolescents and adults. Possible mechanisms and groups that could benefit from vaccinations and other prevention strategies should be investigated.

Funding

St. Olav's University Hospital and Norwegian University of Science and Technology, Norwegian Research Council, Liaison Committee for education, research and innovation in Central Norway, European Commission, Academy of Finland, Sigrid Jusélius Foundation, Foundation for Pediatric Research, and Signe and Ane Gyllenberg Foundation.

Effect of neonatal and adult sepsis on inflammation-related diseases in multiple physiological systems: a Mendelian randomization study

Background

Long-term impact of sepsis on whole body systems is not well investigated. The aim of the study was to explore the potential association of neonatal/adult sepsis with several inflammation-related diseases in multiple physiological systems.

Methods

Instrumental variables for neonatal and adult sepsis were collected from the public genome-wide association studies, which must satisfy the correlation, exclusivity and independence assumptions. Mendelian randomization methods (including random-effect inverse-variance weighted, MR-PRESSO, weighted median and MR-Egger) were used to determine the genetic association of neonatal/adult sepsis with asthma, allergy, rheumatoid arthritis, body mass index/obesity, type 1/type 2 diabetes and intelligence/dementia. Sensitivity analyses were conducted to assess heterogeneity and horizontal pleiotropy. The study was performed by TwoSampleMR in R software.

Results

The inverse-variance weighted method reported that neonatal sepsis was related to the decreased level of body mass index (OR = 0.988, 95%CI = 0.980 ~ 0.997, P = 0.007), and adult sepsis was related to the decreased risk of obesity (OR = 0.785, 95%CI = 0.655 ~ 0.940, P = 0.009). These results were supported by the other Mendelian randomization methods. In addition, the study did not find any association of neonatal/adult sepsis with the other inflammation-related diseases. No heterogeneity and horizontal pleiotropy were found using sensitivity analyses.

Conclusion

Sepsis had the potential to reduce the risk of obesity or body mass index level at a genetic level, both in neonates and in adults.

The bidirectional interaction of COVID-19 infections and lipoproteins

COVID-19 infections decrease total cholesterol, LDL-C, HDL-C, and apolipoprotein A-I, A-II, and B levels while triglyceride levels may be increased or inappropriately normal for the poor nutritional status. The degree of reduction in total cholesterol, LDL-C, HDL-C, and apolipoprotein A-I are predictive of mortality. With recovery lipid/lipoprotein levels return towards pre-infection levels and studies have even suggested an increased risk of dyslipidemia post-COVID-19 infection. The potential mechanisms for these changes in lipid and lipoprotein levels are discussed. Decreased HDL-C and apolipoprotein A-I levels measured many years prior to COVID-19 infections are associated with an increased risk of severe COVID-19 infections while LDL-C, apolipoprotein B, Lp (a), and triglyceride levels were not consistently associated with an increased risk. Finally, data suggest that omega-3-fatty acids and PCSK9 inhibitors may reduce the severity of COVID-19 infections. Thus, COVID-19 infections alter lipid/lipoprotein levels and HDL-C levels may affect the risk of developing COVID-19 infections.

Externalizing traits: Shared causalities for COVID-19 and Alzheimer's dementia using Mendelian randomization analysis

Externalizing traits have been related with the outcomes of coronavirus disease 2019 (COVID-19) and Alzheimer's dementia (AD); however, whether these associations are causal remains unknown. We used the two-sample Mendelian randomization (MR) approach with more than 200 single-nucleotide polymorphisms (SNPs) for externalizing traits to explore the causal associations of externalizing traits with the risk of COVID-19 (infected COVID-19, hospitalized COVID-19, and severe COVID-19) or AD based on the summary data. The inverse variance-weighted method (IVW) was used to estimate the main effect, followed by several sensitivity analyses. IVW analysis showed significant associations of externalizing traits with COVID-19 infection (odds ratio [OR] = 1.456, 95% confidence interval [95% CI] = 1.224-1.731), hospitalized COVID-19 (OR = 1.970, 95% CI = 1.374-2.826), and AD (OR = 1.077, 95% CI = 1.037-1.119). The results were consistent using weighted median (WM), penalized weighted median (PWM), MR-robust adjusted profile score (MR-RAPS), and leave-one-out sensitivity analyses. Our findings assist in exploring the causal effect of externalizing traits on the pathophysiology of infection and severe infection of COVID-19 and AD. Furthermore, our study provides evidence that shared externalizing traits underpin the two diseases.

Causal effects of gut microbiota on sepsis: a two-sample Mendelian randomization study

Background

Recent studies had provided evidence that the gut microbiota is associated with sepsis. However, the potential causal relationship remained unclear.

Methods

The present study aimed to explore the causal effects between gut microbiota and sepsis by performing Mendelian randomization (MR) analysis utilizing publicly accessible genome-wide association study (GWAS) summary-level data. Gut microbiota GWAS (N = 18,340) were obtained from the MiBioGen study and GWAS-summary-level data for sepsis were gained from the UK Biobank (sepsis, 10,154 cases; 452,764 controls). Two strategies were used to select genetic variants, i.e., single nucleotide polymorphisms (SNPs) below the locus-wide significance level (1 × 10^-5) and the genome-wide statistical significance threshold (5 × 10^-8) were chosen as instrumental variables (IVs). The inverse variance weighted (IVW) was used as the primary method for MR study, supplemented by a series of other methods. Additionally, a set of sensitivity analysis methods, including the MR-Egger intercept test, Mendelian randomized polymorphism residual and outlier (MR-PRESSO) test, Cochran's Q test, and leave-one-out test, were carried out to assess the robustness of our findings.

Results

Our study suggested that increased abundance of Deltaproteobacteria, Desulfovibrionales, Catenibacterium, and Hungatella were negatively associated with sepsis risk, while Clostridiaceae1, Alloprevotella, LachnospiraceaeND3007group, and Terrisporobacter were positively correlated with the risk of sepsis. Sensitivity analysis revealed no evidence of heterogeneity and pleiotropy.

Conclusion

This study firstly found suggestive evidence of beneficial or detrimental causal associations of gut microbiota on sepsis risk by applying MR approach, which may provide valuable insights into the pathogenesis of microbiota-mediated sepsis and strategies for sepsis prevention and treatment.

Coronavirus Disease 2019, Diabetes, and Inflammation: A Systemic Review

People with cardiometabolic diseases [namely type 2 diabetes (T2D), obesity, or metabolic syndrome] are more susceptible to coronavirus disease 2019 (COVID-19) infection and endure more severe illness and poorer outcomes. Hyperinflammation has been suggested as a common pathway for both diseases. To examine the role of inflammatory biomarkers shared between COVID-19 and cardiometabolic diseases, we reviewed and evaluated published data using PubMed, SCOPUS, and World Health Organization COVID-19 databases for English articles from December 2019 to February 2022. Of 248 identified articles, 50 were selected and included. We found that people with diabetes or obesity have (i) increased risk of COVID-19 infection; (ii) increased risk of hospitalization (those with diabetes have a higher risk of intensive care unit admissions) and death; and (iii) heightened inflammatory and stress responses (hyperinflammation) to COVID-19, which worsen their prognosis. In addition, COVID-19-infected patients have a higher risk of developing T2D, especially if they have other comorbidities. Treatments controlling blood glucose levels and or ameliorating the inflammatory response may be valuable for improving clinical outcomes in these patient populations. In conclusion, it is critical for health care providers to clinically evaluate hyperinflammatory states to drive clinical decisions for COVID-19 patients.

Proteome-wide Mendelian randomization implicates nephronectin as an actionable mediator of the effect of obesity on COVID-19 severity

Obesity is a major risk factor for Coronavirus disease (COVID-19) severity; however, the mechanisms underlying this relationship are not fully understood. As obesity influences the plasma proteome, we sought to identify circulating proteins mediating the effects of obesity on COVID-19 severity in humans. Here, we screened 4,907 plasma proteins to identify proteins influenced by body mass index using Mendelian randomization. This yielded 1,216 proteins, whose effect on COVID-19 severity was assessed, again using Mendelian randomization. We found that an s.d. increase in nephronectin (NPNT) was associated with increased odds of critically ill COVID-19 (OR = 1.71, P = 1.63 × 10^-10). The effect was driven by an NPNT splice isoform. Mediation analyses supported NPNT as a mediator. In single-cell RNA-sequencing, NPNT was expressed in alveolar cells and fibroblasts of the lung in individuals who died of COVID-19. Finally, decreasing body fat mass and increasing fat-free mass were found to lower NPNT levels. These findings provide actionable insights into how obesity influences COVID-19 severity.

Genome-wide Mendelian randomization and single-cell RNA sequencing analyses identify the causal effects of COVID-19 on 41 cytokines

The elevated levels of inflammatory cytokines have attracted much attention during the treatment of COVID-19 patients. The conclusions of current observational studies are often controversial in terms of the causal effects of COVID-19 on various cytokines because of the confounding factors involving underlying diseases. To resolve this problem, we conducted a Mendelian randomization analysis by integrating the GWAS data of COVID-19 and 41 cytokines. As a result, the levels of 2 cytokines were identified to be promoted by COVID-19 and had unsignificant pleiotropy. In comparison, the levels of 10 cytokines were found to be inhibited and had unsignificant pleiotropy. Among down-regulated cytokines, CCL2, CCL3 and CCL7 were members of CC chemokine family. We then explored the potential molecular mechanism for a significant causal association at a single cell resolution based on single-cell RNA data, and discovered the suppression of CCL3 and the inhibition of CCL3-CCR1 interaction in classical monocytes (CMs) of COVID-19 patients. Our findings may indicate that the capability of COVID-19 in decreasing the chemotaxis of lymphocytes by inhibiting the CCL3-CCR1 interaction in CMs.

Being Physically Active Leads to Better Recovery Prognosis for People Diagnosed with COVID-19: A Cross-Sectional Study

The regular practice of physical activity helps in the prevention and control of several non-communicable diseases. However, evidence on the role of physical activity in mitigating worsening clinical outcomes in people with COVID-19 is still unclear. The aim of this study was to verify whether different levels of physical activity provide protection for clinical outcomes caused by SARS-CoV-2 infection. A cross-sectional study was conducted with 509 adults (43.8 ± 15.71 years; 61.1% female) with a positive diagnosis of COVID-19 residing in Ribeirão Preto, São Paulo, Brazil. Participants were interviewed by telephone to determine the severity of the infection and the physical activity performed. Binary logistic regression was used to indicate the odds ratio (OR) of active people reporting less harmful clinical outcomes from COVID-19. Active people had a lower chance of hospitalization, fewer hospitalization days, less respiratory difficulty and needed less oxygen support. The results suggest that active people, compared to sedentary people, have a lower frequency of hospitalization, length of stay, breathing difficulty and need for oxygen support. These results corroborate the importance of public policies to promote the practice of physical activity, in order to mitigate the severity of the clinical outcomes of COVID-19.

The association of obesity-related traits on COVID-19 severity and hospitalization is affected by socio-economic status: a multivariable Mendelian randomization study

BACKGROUND

Due to its large impact on human health, socio-economic status (SES) could at least partially influence the established association between obesity and coronavirus disease 2019 (COVID-19) severity. To estimate the independent effect of body size and SES on the clinical manifestations of COVID-19, we conducted a Mendelian randomization (MR) study.

METHODS

Applying two-sample MR approaches, we evaluated the effects of body mass index (BMI, n = 322 154), waist circumference (WC, n = 234 069), hip circumference (n = 213 019) and waist-hip ratio (n = 210 088) with respect to three COVID-19 outcomes: severe respiratory COVID-19 (cases = 8779, controls = 1 000 875), hospitalized COVID-19 (cases = 17 992, controls = 1 810 493) and COVID-19 infection (cases = 87 870, controls = 2 210 804). Applying a multivariable MR (MVMR) approach, we estimated the effect of these anthropometric traits on COVID-19 outcomes accounting for the effect of SES assessed as household income (n = 286 301).

RESULTS

BMI and WC were associated with severe respiratory COVID-19 [BMI: odds ratio (OR) = 1.51, CI = 1.24-1.84, P = 3.01e-05; WC: OR = 1.48, 95% CI = 1.15-1.91, P = 0.0019] and hospitalized COVID-19 (BMI: OR = 1.50, 95% CI = 1.32-1.72, P = 8.83e-10; WC: OR = 1.41, 95% CI = 1.20-1.67, P = 3.72e-05). Conversely, income was associated with lower odds of severe respiratory (OR = 0.70, 95% CI = 0.53-0.93, P = 0.015) and hospitalized COVID-19 (OR = 0.78, 95% CI = 0.66-0.92, P = 0.003). MVMR analyses showed that the effect of these obesity-related traits on increasing the odds of COVID-19 negative outcomes becomes null when accounting for income. Conversely, the association of income with lower odds of COVID-19 negative outcomes is not affected when including the anthropometric traits in the multivariable model.

CONCLUSION

Our findings indicate that SES contributes to the effect of obesity-related traits on COVID-19 severity and hospitalization.

The UK Biobank: A Shining Example of Genome-Wide Association Study Science with the Power to Detect the Murky Complications of Real-World Epidemiology

Genome-wide association studies (GWASs) have successfully identified thousands of genetic variants that are reliably associated with human traits. Although GWASs are restricted to certain variant frequencies, they have improved our understanding of the genetic architecture of complex traits and diseases. The UK Biobank (UKBB) has brought substantial analytical opportunity and performance to association studies. The dramatic expansion of many GWAS sample sizes afforded by the inclusion of UKBB data has improved the power of estimation of effect sizes but, critically, has done so in a context where phenotypic depth and precision enable outcome dissection and the application of epidemiological approaches. However, at the same time, the availability of such a large, well-curated, and deeply measured population-based collection has the capacity to increase our exposure to the many complications and inferential complexities associated with GWASs and other analyses. In this review, we discuss the impact that UKBB has had in the GWAS era, some of the opportunities that it brings, and exemplar challenges that illustrate the reality of using data from this world-leading resource.

Genetic Predisposition to Severe COVID-19 Might Increase the Risk of Stroke: A Two-Sample Mendelian Randomization Study

Aims: The causal relationship between COVID-19 infection and stroke has not yet been fully established. This study aimed to explore this causality using two-sample Mendelian randomization (MR).

Materials and Methods: Genetic variants associated with COVID-19 infection and stroke were both obtained from genome-wide association study (GWAS) summary data. The single nucleotide polymorphisms (SNPs) were selected as instrumental variables. The standard inverse variance weighted (IVW) was primarily used to assess this causality. Finally, sensitivity analysis was performed to evaluate the reliability and stability.

Results: The results showed that being hospitalized due to COVID-19 had a positive effect on stroke [OR = 1.05; 95% CI= (1.01, 1.10); p = 2.34 × 10⁻⁵] and ischemic stroke [OR = 1.06; 95% CI= (1.02, 1.11); p = 2.28 × 10⁻⁶] analyzed by inverse variance weighted. Moreover, the results revealed that severe respiratory symptoms due to COVID-19 had a positive effect on stroke [OR = 1.04; 95% CI= (1.00, 1.06); p = 0.04] and that the causal effect of severe respiratory symptoms due to COVID-19 on ischemic stroke estimated by IVW suggested a positive effect [OR = 1.06; 95% CI= (1.02, 1.09); p = 0.0068], too.

Conclusion: In summary, this study showed that severe COVID-19 might increase the risk of stroke, thus much more attention should be paid to patients with severe COVID-19.

Review of the British Thoracic Society Winter Meeting 2021, 24-26 November 2021

The Winter Meeting of the British Thoracic Society (BTS) is a platform for the latest clinical and scientific research in respiratory medicine. This review summarises the key symposia and presentations from the BTS Winter Meeting 2021 held online due to the COVID-19 pandemic.

Acceptance Rates of COVID-19 Vaccine Highlight the Need for Targeted Public Health Interventions

We aimed to examine rates of COVID-19 vaccination to elucidate the need for targeted public health interventions. We retrospectively reviewed the electronic medical files of all adults registered in a central district in Israel from 1 January 2021 to 31 March 2022. The population was characterized by vaccination status against COVID-19 and the number of doses received. Univariate and multivariable analyses were used to identify predictors of low vaccination rates that required targeted interventions. Of the 246,543 subjects included in the study, 207,911 (84.3%) were vaccinated. The minority groups of ultra-Orthodox Jews and Arabs had lower vaccination rates than the non-ultra-Orthodox Jews (68.7%, 80.5% and 87.7%, respectively, p < 0.001). Adults of low socioeconomic status (SES) had lower vaccination rates compared to those of high SES (74.4% vs. 90.8%, p < 0.001). Adults aged 20−59 years had a lower vaccination rate than those ≥60 years (80.0% vs. 92.1%, p < 0.0001). Multivariate analysis identified five independent variables that were significantly (p < 0.001) associated with low vaccination rates: minority groups of the ultra-Orthodox sector and Arab population, and underlying conditions of asthma, smoking and diabetes mellitus (odds ratios: 0.484, 0.453, 0.843, 0.901 and 0.929, respectively). Specific targeted public health interventions towards these subpopulations with significantly lower rates of vaccination are suggested.

Mendelian Randomization Analysis Reveals No Causal Relationship Between Nonalcoholic Fatty Liver Disease and Severe COVID-19

BACKGROUND & AIMS

The coronavirus disease 2019 (COVID-19) pandemic has witnessed more than 4.5 million deaths as of the time of writing. Whether nonalcoholic fatty liver disease (NAFLD) increases the risk for severe COVID-19 remains unclear. We sought to address this question using 2-sample Mendelian randomization (TSMR) analysis approaches in large cohorts.

METHODS

We performed large-scale TSMR analyses to examine whether there is a causal relationship between NAFLD, serum alanine aminotransferase, grade of steatosis, NAFLD Activity Score, or fibrosis stage and severe COVID-19. To maximize the power of this analysis, we performed a genome-wide meta-analysis to identify single nucleotide polymorphisms associated with NAFLD. We also examined the impact of 20 major comorbid factors of NAFLD on severe COVID-19.

RESULTS

Univariate analysis of the UK Biobank data demonstrated a significant association between NAFLD and severe COVID-19 (odds ratio [OR], 3.06; P = 1.07 × 10^-6). However, this association disappeared after demographic and comorbid factors were adjusted (OR, 1.57; P = .09). TSMR study indicated that NAFLD (OR, 0.97; P = .61), alanine aminotransferase level (OR, 1.03; P = .47), grade of steatosis (OR, 1.08; P = .41), NAFLD Activity Score (OR, 1.02; P = .39), and fibrosis stage (OR, 1.01; P = .87) were not associated with severe COVID-19. Among all NAFLD-related comorbid factors, body mass index (OR, 1.73; P = 7.65 × 10^-9), waist circumference (OR, 1.76; P = 2.58 × 10^-5), and hip circumference (OR, 1.33; P = 7.26 × 10^-3) were the only ones demonstrated a causal impact on severe COVID-19.

CONCLUSIONS

There is no evidence supporting that NAFLD is a causal risk factor for severe COVID-19. Previous observational associations between NAFLD and COVID-19 are likely attributed to the correlation between NAFLD and obesity.

Association of smoking, lung function and COPD in COVID-19 risk: a two-step Mendelian randomization study

BACKGROUND AND AIMS

Smoking increases the risk of severe COVID-19, but whether lung function or chronic obstructive pulmonary disease (COPD) mediate the underlying associations is unclear. We conducted the largest Mendelian randomization study to date, to our knowledge, to address these questions.

DESIGN

Mendelian randomization study using summary statistics from genome-wide association studies (GWAS), FinnGen and UK Biobank. The main analysis was the inverse variance weighted method, and we included a range of sensitivity analyses to assess the robustness of the findings.

SETTING

GWAS which included international consortia, FinnGen and UK Biobank.

PARTICIPANTS

The sample size ranged from 193 638 to 2 586 691.

MEASUREMENTS

Genetic determinants of life-time smoking index, lung function [e.g. forced expiratory volume in 1 sec (FEV₁ )], chronic obstructive pulmonary disease (COPD) and different severities of COID-19.

RESULTS

Smoking increased the risk of COVID-19 compared with population controls for overall COVID-19 [odds ratio (OR) = 1.19 per standard deviation (SD) of life-time smoking index, 95% confidence interval (CI) = 1.11-1.27], hospitalized COVID-19 (OR = 1.67, 95% CI = 1.42-1.97) or severe COVID-19 (OR = 1.48, 95% CI = 1.10-1.98), with directionally consistent effects from sensitivity analyses. Lung function and COPD liability did not appear to mediate these associations.

CONCLUSION

There is genetic evidence that smoking probably increases the risk of severe COVID-19 and possibly also milder forms of COVID-19. Decreased lung function and increased risk of chronic obstructive pulmonary disease do not seem to mediate the effect of smoking on COVID-19 risk.

Human Serum Metabolites as Potential Mediators from Type 2 Diabetes and Obesity to COVID-19 Severity and Susceptibility: Evidence from Mendelian Randomization Study

Obesity, type 2 diabetes (T2D), and severe coronavirus disease 2019 (COVID-19) are closely associated. The aim of this study was to elucidate the casual and mediating relationships of human serum metabolites on the pathways from obesity/T2D to COVID-19 using Mendelian randomization (MR) techniques. We performed two-sample MR to study the causal effects of 309 metabolites on COVID-19 severity and susceptibility, based on summary statistics from genome-wide association studies (GWAS) of metabolites (n = 7824), COVID-19 phenotypes (n = 2,586,691), and obesity (n = 322,154)/T2D traits (n = 898,130). We conducted two-sample network MR analysis to determine the mediating metabolites on the causal path from obesity/T2D to COVID-19 phenotypes. We used multivariable MR analysis (MVMR) to discover causal metabolites independent of body mass index (BMI). Our MR analysis yielded four causal metabolites that increased the risk of severe COVID-19, including 2-stearoylglycerophosphocholine (OR 2.15; 95% CI 1.48–3.11), decanoylcarnitine (OR 1.32; 95% CI 1.17–1.50), thymol sulfate (OR 1.20; 95% CI 1.10–1.30), and bradykinin-des-arg(9) (OR 1.09; 95% CI 1.05–1.13). One significant mediator, gamma-glutamyltyrosine, lay on the causal path from T2D/obesity to severe COVID-19, with 16.67% (0.64%, 32.70%) and 6.32% (1.76%, 10.87%) increased risk, respectively, per one-standard deviation increment of genetically predicted T2D and BMI. Our comprehensive MR analyses identified credible causative metabolites, mediators of T2D and obesity, and obesity-independent causative metabolites for severe COVID-19. These biomarkers provide a novel basis for mechanistic studies for risk assessment, prognostication, and therapeutic purposes in COVID-19.

Circulating Polyunsaturated Fatty Acids and COVID-19: A Prospective Cohort Study and Mendelian Randomization Analysis

Higher circulating polyunsaturated fatty acids (PUFAs), especially omega-3 fatty acids, have been linked to a better prognosis in patients of coronavirus disease 2019 (COVID-19). However, the effects and causality of pre-infection PUFA levels remain unclear. This study aimed to investigate the observational and causal associations of circulating PUFAs with COVID-19 susceptibility and severity. We first performed a prospective cohort study in UK Biobank, with 20,626 controls who were tested negative and 4,101 COVID-19 patients, including 970 hospitalized ones. Plasma PUFAs at baseline (blood samples collected from 2007 to 2010) were measured by nuclear magnetic resonance, including total PUFAs, omega-3 PUFAs, omega-6 PUFAs, docosahexaenoic acid (DHA), linoleic acid (LA), and the omega-6/omega-3 ratio. Moreover, going beyond UK Biobank, we leveraged summary statistics from existing genome-wide association studies to perform bidirectional two-sample Mendelian randomization (MR) analyses to examine the causal associations of eight individual PUFAs, measured in either plasma or red blood cells, with COVID-19 susceptibility and severity. In the observational association analysis of each PUFA measure separately, total, omega-3, and omega-6 PUFAs, DHA, and LA were associated with a lower risk of severe COVID-19. Omega-3 PUFAs and DHA were also associated with a lower risk of testing positive for COVID-19. The omega-6/omega-3 ratio was positively associated with risks of both susceptibility and severity. When omega-6, omega-3, and their ratio are jointly analyzed, only omega-3 PUFAs remained significantly and inversely associated with both susceptibility and severity. The forward MR analysis indicated that docosapentaenoic acid (DPA-n3) and arachidonic acid (AA) might be causally associated with a lower risk of severe COVID-19, with OR (95% CI) per one SD increase in the plasma level as 0.89 (0.81, 0.99) and 0.96 (0.94, 0.99), respectively. The reverse MR analysis did not support any causal effect of COVID-19 on PUFAs. Our observational analysis supported that higher circulating omega-3 PUFAs, especially DHA, may lower the susceptibility to and alleviate the severity of COVID-19. Our MR analysis further supported causal associations of DPA-n3 and AA with a lower risk of severe COVID-19.

Thyroid function and risk of bloodstream infections: Results from the Norwegian prospective population-based HUNT Study

OBJECTIVE

Previous studies on thyroid function and risk of infection is conflicting and often stem from intensive care cohorts were nonthyroidal illness syndrome (NTIS) may be present. The objective of this study was to identify the risk of bloodstream infections (BSI) and BSI-related mortality with thyroid-stimulating hormone (TSH) levels within the reference range in a general population.

DESIGN

Prospective follow-up.

PARTICIPANTS

The HUNT2 (1995-97) included 34,619 participants with information on TSH levels.

MEASUREMENTS

Hazard ratios (HRs) with 95% confidence interval (CI) confirmed BSIs and BSI-related mortality until 2011.

RESULTS

During a median follow-up of 14.5 years, 1179 experienced at least one episode of BSI and 208 died within 30 days after a BSI. TSH levels within the reference range of 0.5-4.5 mU/L were not associated with the risk of first-time BSI, with an HR of 0.97 (95% CI: 0.90-1.04) per mU/L. Stratified by baseline age < or ≥65 years, TSH was inversely associated with the risk of BSI (HR: 0.88; 95% CI: 0.78-1.00 per mU/L) in the youngest age group only. Persons with any baseline thyroid disease had a 30% risk and the hyperthyroid subgroup a 57%, and hypothyroidism a 20% increased risk of BSI. TSH levels were not clearly associated with BSI mortality, but the HRs were imprecise due to few BSI-related deaths.

CONCLUSION

There was some evidence of a weak inverse association between TSH levels and the risk of BSI in persons below 65 years of age. The increased risk seen in persons with thyroid illness is probably explained by confounding by concurrent ill health.

Causal relationship between physical activity, leisure sedentary behaviors and COVID-19 risk: a Mendelian randomization study

BACKGROUND

The 2019 coronavirus disease pandemic (COVID-19) poses an enormous threat to public health worldwide, and the ensuing management of social isolation has greatly decreased opportunities for physical activity (PA) and increased opportunities for leisure sedentary behaviors (LSB). Given that both PA and LSB have been established as major influencing factors for obesity, diabetes and cardiometabolic syndrome, whether PA/LSB in turn affects the susceptibility to COVID-19 by disrupting metabolic homeostasis remains to be explored. In this study, we aimed to systematically evaluate the causal relationship between PA/LSB and COVID-19 susceptibility, hospitalization and severity using a Mendelian randomization study.

METHODS

Data were obtained from a large-scale PA dataset (N = 377,000), LSB dataset (N = 422,218) and COVID-19 Host Genetics Initiative (N = 2,586,691). The causal effects were estimated with inverse variance weighted, MR-Egger, weighted median and MR-PRESSO. Sensitivity analyses were implemented with Cochran's Q test, MR-Egger intercept test, MR-PRESSO, leave-one-out analysis and the funnel plot. Risk factor analyses were further conducted to investigate the potential mediators.

RESULTS

Genetically predicted accelerometer-assessed PA decreased the risk for COVID-19 hospitalization (OR = 0.93, 95% CI 0.88-0.97; P = 0.002), while leisure television watching significantly increased the risk of COVID-19 hospitalization (OR = 1.55, 95% CI 1.29-1.88; P = 4.68 × 10-6) and disease severity (OR = 1.85, 95% CI 1.33-2.56; P = 0.0002) after Bonferroni correction. No causal effects of self-reported moderate to vigorous physical activity (MVPA), accelerometer fraction of accelerations > 425 milligravities, computer use or driving on COVID-19 progression were observed. Risk factor analyses indicated that the above causal associations might be mediated by several metabolic risk factors, including smoking, high body mass index, elevated serum triglyceride levels, insulin resistance and the occurrence of type 2 diabetes.

CONCLUSION

Our findings supported a causal effect of accelerometer-assessed PA on the reduced risk of COVID-19 hospitalization as well as television watching on the increased risk of COVID-19 hospitalization and severity, which was potentially mediated by smoking, obesity and type 2 diabetes-related phenotypes. Particular attention should be given to reducing leisure sedentary behaviors and encouraging proper exercise during isolation and quarantine for COVID-19.

Effects of COVID-19 on Blood Culture Contamination at a Tertiary Care Academic Medical Center

The COVID-19 pandemic has changed health care, from increased needs of personal protective equipment (PPE) to overloaded staff and influxes of patients. Blood cultures are frequently used to detect bloodstream infections in critically ill patients, but it is unknown whether the COVID-19 pandemic has had an impact on blood culture contamination rates. A total of 88,332 blood cultures taken over a 33-month period were analyzed to compare blood culture contamination rates before the COVID-19 pandemic to rates during the pandemic. A significant increase in the average number of monthly nurse-drawn and peripherally collected cultures occurred after the start of the pandemic, but there was a decrease in the average number of phlebotomy cultures. A significant increase in contamination rates (P < 0.001) was found in all nonemergency hospital departments during the COVID-19 pandemic, increasing from 2.1% to 2.5%. Increased rates during the COVID-19 pandemic were also found for nursing staff (2.0% to 2.4%) and both peripheral (2.1% to 2.5%) and indwelling line draws (1.1% to 1.7). The number of cultures drawn monthly increased in acute adult departments and both adult and pediatric emergency departments. Blood culture contamination rates in adult acute, adult emergency, and pediatric intensive care units increased after the start of the pandemic by 23%, 75%, and 59%, respectively. A positive correlation was found between blood culture contamination rates and COVID-19 incidence rates. Additional periodic staff training on proper blood collection technique and awareness of the workload of health care workers are recommended to decrease contamination rates during the COVID-19 pandemic. IMPORTANCE Understanding factors that contribute to blood culture contamination is important in order to take steps to limit contamination events. Here, we examine the effect the COVID-19 pandemic has had on blood culture contamination rates and specifically detail the effects based on the staff, draw types, and unit types. The conclusions provided here can be used as hospitals and laboratories navigate the COVID-19 pandemic or other times of high patient volume.

Identifying factors contributing to increased susceptibility to COVID-19 risk: a systematic review of Mendelian randomization studies

Background

To summarize modifiable factors for coronavirus disease 2019 (COVID-19) suggested by Mendelian randomization studies.

Methods

In this systematic review, we searched PubMed, EMBASE and MEDLINE, from inception to 15 November 2021, for Mendelian randomization studies in English. We selected studies that assessed associations of genetically predicted exposures with COVID-19-related outcomes (severity, hospitalization and susceptibility). Risk of bias of the included studies was evaluated based on the consideration of the three main assumptions for instrumental variable analyses.

Results

We identified 700 studies through systematic search, of which 50 Mendelian randomization studies were included. Included studies have explored a wide range of socio-demographic factors, lifestyle attributes, anthropometrics and biomarkers, predisposition to diseases and druggable targets in COVID-19 risk. Mendelian randomization studies suggested that increases in smoking, obesity and inflammatory factors were associated with higher risk of COVID-19. Predisposition to ischaemic stroke, combined bipolar disorder and schizophrenia, attention-deficit and hyperactivity disorder, chronic kidney disease and idiopathic pulmonary fibrosis was potentially associated with higher COVID-19 risk. Druggable targets, such as higher protein expression of histo-blood group ABO system transferase (ABO), interleukin (IL)-6 and lower protein expression of 2′-5′ oligoadenylate synthetase 1 (OAS1) were associated with higher risk of COVID-19. There was no strong genetic evidence supporting the role of vitamin D, glycaemic traits and predisposition to cardiometabolic diseases in COVID-19 risk.

Conclusion

This review summarizes modifiable factors for intervention (e.g. smoking, obesity and inflammatory factors) and proteomic signatures (e.g. OAS1 and IL-6) that could help identify drugs for treating COVID-19.

Relationship Between Plasma Vitamin C and COVID-19 Susceptibility and Severity: A Two-Sample Mendelian Randomization Study

Background

Considering the antioxidant function of Vitamin C, also called ascorbic acid, it is widely used against viral infections such as coronavirus disease (COVID-19) based on in vitro, observational, and ecological studies. Many confounding factors that can affect Vitamin C levels. Thus, the association described to date may not be causal. To determine the causal relationship between genetically predicted plasma Vitamin C and COVID-19 susceptibility and severity, we performed two-sample Mendelian randomization (MR) based on large samples.

Methods

The summary-level data for Vitamin C was obtained from a GWAS meta-analysis, which included 52,018 individuals from four studies of European ancestry. Data for COVID-19 HGI results were obtained from the meta-analysis of 35 GWASs with more than 1,000,000 subjects of European ancestry, including 32,494 cases with COVID-19 susceptibility and 1,316,207 controls, 9,986 cases with COVID-19 hospitalization and 1,877,672 controls, and 5,101 cases with COVID-19 severe disease and 1,383,241 controls. Mendelian randomization (MR) analysis was conducted to examine the effect of selected single nucleotide polymorphisms and COVID-19 susceptibility, hospitalization, disease severity. Several sensitivity analyses were performed with inverse-variance weighted (random-effect model), inverse variance weighted (fixed-effect model), weighted median, and maximum likelihood methods for estimating the causal effects.

Results

In this MR study, genetic predisposition to the levels of plasma Vitamin C was not associated with COVID-19 susceptibility (OR: 0.99, 95% CI: 0.84–1.17, P = 0.91), hospitalization (OR: 1.10, 95% CI: 0.71–1.71, P = 0.67) and severity (OR: 0.83, 95% CI: 0.43–1.59, P = 0.58). The association was consistent in complementary analyses. No potential heterogeneities and directional pleiotropies were observed for the analysis results.

Conclusion

According to our study, no correlation was observed between plasma Vitamin C levels and COVID-19 susceptibility and severity. Further studies in different ethnics are necessary to explore the potential role and mechanisms of circulating serum Vitamin C levels on COVID-19.

COVID-19 Risk Stratification and Mortality Prediction in Hospitalized Indian Patients: Harnessing clinical data for public health benefits

The variability of clinical course and prognosis of COVID-19 highlights the necessity of patient sub-group risk stratification based on clinical data. In this study, clinical data from a cohort of Indian COVID-19 hospitalized patients is used to develop risk stratification and mortality prediction models. We analyzed a set of 70 clinical parameters including physiological and hematological for developing machine learning models to identify biomarkers. We also compared the Indian and Wuhan cohort, and analyzed the role of steroids. A bootstrap averaged ensemble of Bayesian networks was also learned to construct an explainable model for discovering actionable influences on mortality and days to outcome. We discovered blood parameters, diabetes, co-morbidity and SpO2 levels as important risk stratification features, whereas mortality prediction is dependent only on blood parameters. XGboost and logistic regression model yielded the best performance on risk stratification and mortality prediction, respectively (AUC score 0.83, AUC score 0.92). Blood coagulation parameters (ferritin, D-Dimer and INR), immune and inflammation parameters IL6, LDH and Neutrophil (%) are common features for both risk and mortality prediction. Compared with Wuhan patients, Indian patients with extreme blood parameters indicated higher survival rate. Analyses of medications suggest that a higher proportion of survivors and mild patients who were administered steroids had extreme neutrophil and lymphocyte percentages. The ensemble averaged Bayesian network structure revealed serum ferritin to be the most important predictor for mortality and Vitamin D to influence severity independent of days to outcome. The findings are important for effective triage during strains on healthcare infrastructure.

Predictors of Covid-19 level of concern among older adults from the health and retirement study

The purpose of this longitudinal study is to construct a prediction model for Covid-19 level of concern using established Covid-19 socio-demographic, lifestyle and health risk characteristics and to examine specific contributions of obesity-related cardiometabolic health characteristics as predictors of Covid-19 level of concern among a representative sample of U.S. older adults. We performed secondary analyses of existing data on 2872 2006–2020 Health and Retirement Study participants and examined 19 characteristics in relation to the outcome of interest using logistic regression and machine learning algorithms. In mixed-effects ordinal logistic regression models, a history of diabetes, stroke as well as 1–2 cardiometabolic risk factors and/or chronic conditions were associated with greater Covid-19 level of concern, after controlling for confounders. Female sex, birth cohort, minority race, Hispanic ethnicity and total wealth as well as depressive symptoms were associated with higher level of Covid-19 concern, and education was associated with lower level of Covid-19 concern in fully adjusted mixed-effects ordinal logistic regression models. The selected socio-demographic, lifestyle and health characteristics accounted for < 70% of the variability in Covid-19 level of concern based on machine learning algorithms. Independent risk factors for Covid-19 level of concern among U.S. older adults include socio-demographic characteristics and depressive symptoms. Advanced research is needed to identify relevant predictors and elucidate underlying mechanisms of observed relationships.

Circulating polyunsaturated fatty acids and COVID-19: a prospective cohort study and Mendelian randomization analysis

Background:

Higher circulating polyunsaturated fatty acids (PUFAs), especially omega-3 ones, have been linked to a better prognosis in patients of coronavirus disease 2019 (COVID-19). However, the effects and causality of pre-infection PUFA levels remain unclear.

Objective:

To investigate the observational and causal associations of circulating PUFAs with COVID-19 susceptibility and severity.

Design:

We first performed a prospective cohort study in UK Biobank, with 20,626 controls who were tested negative and 4,101 COVID-19 patients, including 970 hospitalized ones. Plasma PUFAs at baseline were measured by nuclear magnetic resonance, including total PUFAs, omega-3 PUFAs, omega-6 PUFAs, docosahexaenoic acid (DHA), linoleic acid (LA), and the omega-6/omega-3 ratio. Moreover, bidirectional two-sample Mendelian randomization (MR) analyses were performed to examine the causal associations of eight individual PUFAs, measured in either plasma or red blood cells, with COVID-19 susceptibility and severity using summary statistics from existing genome-wide association studies.

Results:

In the observational association analysis, total PUFAs, omega-3 PUFAs, omega-6 PUFAs, DHA, and LA were associated with a lower risk of severe COVID-19. Omega-3 PUFAs and DHA were also associated with a lower risk of testing positive for COVID-19. The omega-6/omega-3 ratio was positively associated with risks of both susceptibility and severity. The forward MR analysis indicated that arachidonic acid (AA) and docosapentaenoic acid (DPA-n3) might be causally associated with a lower risk of severe COVID-19, with OR (95% CI) per one SD increase in the plasma level as 0.96 (0.94, 0.99) and 0.89 (0.81, 0.99), respectively. The reverse MR analysis did not support any causal effect of COVID-19 on PUFAs.

Conclusions:

Our observational analysis supported that higher circulating PUFAs, either omega-3 or omega-6, are protective against severe COVID-19, while omega-3 PUFAs, especially DHA, were also associated with reducing COVID-19 susceptibility. Our MR analysis further supported causal associations of AA and DPA-n3 with a lower risk of severe COVID-19.

Associations between body composition, fat distribution and metabolic consequences of excess adiposity with severe COVID-19 outcomes: observational study and Mendelian randomisation analysis

Background

Higher body mass index (BMI) and metabolic consequences of excess weight are associated with increased risk of severe COVID-19, though their mediating pathway is unclear.

Methods

A prospective cohort study included 435,504 UK Biobank participants. A two-sample Mendelian randomisation (MR) study used the COVID-19 Host Genetics Initiative in 1.6 million participants. We examined associations of total adiposity, body composition, fat distribution and metabolic consequences of excess weight, particularly type 2 diabetes, with incidence and severity of COVID-19, assessed by test positivity, hospital admission, intensive care unit (ICU) admission and death.

Results

BMI and body fat were associated with COVID-19 in the observational and MR analyses but muscle mass was not. The observational study suggested the association with central fat distribution was stronger than for BMI, but there was little evidence from the MR analyses than this was causal. There was evidence that strong associations of metabolic consequences with COVID-19 outcomes in observational but not MR analyses. Type 2 diabetes was strongly associated with COVID-19 in observational but not MR analyses. In adjusted models, the observational analysis showed that the association of BMI with COVID-19 diminished, while central fat distribution and metabolic consequences of excess weight remained strongly associated. In contrast, MR showed the reverse, with only BMI retaining a direct effect on COVID-19.

Conclusions

Excess total adiposity is probably casually associated with severe COVID-19. Mendelian randomisation data do not support causality for the observed associations of central fat distribution or metabolic consequences of excess adiposity with COVID-19.

Genetically Predicted Higher Educational Attainment Decreases the Risk of COVID-19 Susceptibility and Severity: A Mendelian Randomization Study

Background: Prior observational studies indicated that lower educational attainment (EA) is associated with higher COVID-19 risk, while these findings were vulnerable to bias from confounding factors. We aimed to clarify the causal effect of EA on COVID-19 susceptibility, hospitalization, and severity using Mendelian randomization (MR).

Methods: We identified genetic instruments for EA from a large genome-wide association study (GWAS) (n = 1,131,881). Summary statistics for COVID-19 susceptibility (112,612 cases and 2,474,079 controls), hospitalization (24,274 cases and 2,061,529 controls), and severity (8,779 cases and 1,001,875 controls) were obtained from the COVID-19 Host Genetics Initiative. We used the single-variable MR (SVMR) and the multivariable MR (MVMR) controlling intelligence, income, body mass index, vigorous physical activity, sedentary behavior, smoking, and alcohol consumption to estimate the total and direct effects of EA on COVID-19 outcomes. Inverse variance weighted was the primary analysis method. All the statistical analyses were performed using R software.

Results: Results from the SVMR showed that genetically predicted higher EA was correlated with a lower risk of COVID-19 susceptibility [odds ratio (OR) 0.86, 95% CI 0.84–0.89], hospitalization (OR 0.67, 95% CI 0.62–0.73), and severity (OR 0.67, 95% CI 0.58–0.79). EA still maintained its effects in most of the MVMR.

Conclusion: Educational attainment is a predictor for susceptibility, hospitalization, and severity of COVID-19 disease. Population with lower EA should be provided with a higher prioritization to public health resources to decrease the morbidity and mortality of COVID-19.

Mendelian Randomization: Concepts and Scope

Mendelian randomization (MR) is a method of studying the causal effects of modifiable exposures (i.e., potential risk factors) on health, social, and economic outcomes using genetic variants associated with the specific exposures of interest. MR provides a more robust understanding of the influence of these exposures on outcomes because germline genetic variants are randomly inherited from parents to offspring and, as a result, should not be related to potential confounding factors that influence exposure-outcome associations. The genetic variant can therefore be used as a tool to link the proposed risk factor and outcome, and to estimate this effect with less confounding and bias than conventional epidemiological approaches. We describe the scope of MR, highlighting the range of applications being made possible as genetic data sets and resources become larger and more freely available. We outline the MR approach in detail, covering concepts, assumptions, and estimation methods. We cover some common misconceptions, provide strategies for overcoming violation of assumptions, and discuss future prospects for extending the clinical applicability, methodological innovations, robustness, and generalizability of MR findings.

Causal associations between body fat accumulation and COVID-19 severity: A Mendelian randomization study

Previous studies reported associations between obesity measured by body mass index (BMI) and coronavirus disease 2019 (COVID-19). However, BMI is calculated only with height and weight and cannot distinguish between body fat mass and fat-free mass. Thus, it is not clear if one or both of these measures are mediating the relationship between obesity and COVID-19. Here, we used Mendelian randomization (MR) to compare the independent causal relationships of body fat mass and fat-free mass with COVID-19 severity. We identified single nucleotide polymorphisms associated with body fat mass and fat-free mass in 454,137 and 454,850 individuals of European ancestry from the UK Biobank, respectively. We then performed two-sample MR to ascertain their effects on severe COVID-19 (cases: 4,792; controls: 1,054,664) from the COVID-19 Host Genetics Initiative. We found that an increase in body fat mass by one standard deviation was associated with severe COVID-19 (odds ratio (OR)_{body fat mass} = 1.61, 95% confidence interval [CI]: 1.28-2.04, P = 5.51 × 10^-5; OR_{body fat-free mass} = 1.31, 95% CI: 0.99-1.74, P = 5.77 × 10^-2). Considering that body fat mass and fat-free mass were genetically correlated with each other (r = 0.64), we further evaluated independent causal effects of body fat mass and fat-free mass using multivariable MR and revealed that only body fat mass was independently associated with severe COVID-19 (OR_{body fat mass} = 2.91, 95% CI: 1.71-4.96, P = 8.85 × 10^-5 and OR_{body fat-free mass} = 1.02, 95%CI: 0.61-1.67, P = 0.945). In summary, this study demonstrates the causal effects of body fat accumulation on COVID-19 severity and indicates that the biological pathways influencing the relationship between COVID-19 and obesity are likely mediated through body fat mass.

Smoking and COVID-19 outcomes: an observational and Mendelian randomisation study using the UK Biobank cohort

BACKGROUND

Conflicting evidence has emerged regarding the relevance of smoking on risk of COVID-19 and its severity.

METHODS

We undertook large-scale observational and Mendelian randomisation (MR) analyses using UK Biobank. Most recent smoking status was determined from primary care records (70.8%) and UK Biobank questionnaire data (29.2%). COVID-19 outcomes were derived from Public Health England SARS-CoV-2 testing data, hospital admissions data, and death certificates (until 18 August 2020). Logistic regression was used to estimate associations between smoking status and confirmed SARS-CoV-2 infection, COVID-19-related hospitalisation, and COVID-19-related death. Inverse variance-weighted MR analyses using established genetic instruments for smoking initiation and smoking heaviness were undertaken (reported per SD increase).

RESULTS

There were 421 469 eligible participants, 1649 confirmed infections, 968 COVID-19-related hospitalisations and 444 COVID-19-related deaths. Compared with never-smokers, current smokers had higher risks of hospitalisation (OR 1.80, 95% CI 1.26 to 2.29) and mortality (smoking 1-9/day: OR 2.14, 95% CI 0.87 to 5.24; 10-19/day: OR 5.91, 95% CI 3.66 to 9.54; 20+/day: OR 6.11, 95% CI 3.59 to 10.42). In MR analyses of 281 105 White British participants, genetically predicted propensity to initiate smoking was associated with higher risks of infection (OR 1.45, 95% CI 1.10 to 1.91) and hospitalisation (OR 1.60, 95% CI 1.13 to 2.27). Genetically predicted higher number of cigarettes smoked per day was associated with higher risks of all outcomes (infection OR 2.51, 95% CI 1.20 to 5.24; hospitalisation OR 5.08, 95% CI 2.04 to 12.66; and death OR 10.02, 95% CI 2.53 to 39.72).

INTERPRETATION

Congruent results from two analytical approaches support a causal effect of smoking on risk of severe COVID-19.

Extensive Mendelian randomization study identifies potential causal risk factors for severe COVID-19

Background

Identifying causal risk factors for severe coronavirus disease 2019 (COVID-19) is critical for its prevention and treatment. Many associated pre-existing conditions and biomarkers have been reported, but these observational associations suffer from confounding and reverse causation.

Methods

Here, we perform a large-scale two-sample Mendelian randomization (MR) analysis to evaluate the causal roles of many traits in severe COVID-19.

Results

Our results highlight multiple body mass index (BMI)-related traits as risk-increasing: BMI (OR: 1.89, 95% CI: 1.51–2.37), hip circumference (OR: 1.46, 1.15–1.85), and waist circumference (OR: 1.82, 1.36–2.43). Our multivariable MR analysis further suggests that the BMI-related effect might be driven by fat mass (OR: 1.63, 1.03–2.58), but not fat-free mass (OR: 1.00, 0.61–1.66). Several white blood cell counts are negatively associated with severe COVID-19, including those of neutrophils (OR: 0.76, 0.61–0.94), granulocytes (OR: 0.75, 0.601–0.93), and myeloid white blood cells (OR: 0.77, 0.62–0.96). Furthermore, some circulating proteins are associated with an increased risk of (e.g., zinc-alpha-2-glycoprotein) or protection from severe COVID-19 (e.g., prostate-associated microseminoprotein).

Conclusions

Our study suggests that fat mass and white blood cells might be involved in the development of severe COVID-19. It also prioritizes potential risk and protective factors that might serve as drug targets and guide the effective protection of high-risk individuals.

People infected with SARS-CoV-2 can remain asymptomatic, have mild symptoms, or develop severe COVID-19 that increases their risk of death. Finding factors that directly contribute to the risk of developing severe COVID-19, so-called causal risk factors, may help in prevention by identifying individuals at higher risk and in treatment by providing clues of targets for therapies. We applied advanced statistical methods that leverage genetic variations among individuals to separate causal risk factors from coincidences, sifting through tens of thousands of candidate factors. We show that levels of fat mass, certain white blood cells, and multiple circulating proteins are potential causal risk factors for severe COVID-19. These findings help us better understand severe COVID-19 and guide future studies to develop strategies of prevention and treatment.

Sun et al. perform a two-sample Mendelian randomization study of a large number of traits with the aim of identifying risk factors of severe COVID-19. They show that body mass index-related traits, specific white blood cells, and some circulating proteins are risk factors for the development of severe COVID-19.

Causal Effects of Genetically Predicted Iron Status on Sepsis: A Two-Sample Bidirectional Mendelian Randomization Study

Background/Aim: Several observational studies showed a significant association between elevated iron status biomarkers levels and sepsis with the unclear direction of causality. A two-sample bidirectional mendelian randomization (MR) study was designed to identify the causal direction between seven iron status traits and sepsis. Methods: Seven iron status traits were studied, including serum iron, ferritin, transferrin saturation, transferrin, hemoglobin, erythrocyte count, and reticulocyte count. MR analysis was first performed to estimate the causal effect of iron status on the risk of sepsis and then performed in the opposite direction. The multiplicative random-effects and fixed-effects inverse-variance weighted, weighted median-based method and MR-Egger were applied. MR-Egger regression, MR pleiotropy residual sum and outlier (MR-PRESSO), and Cochran's Q statistic methods were used to assess heterogeneity and pleiotropy. Results: Genetically predicted high levels of serum iron (OR = 1.21, 95%CI = 1.13-1.29, p = 3.16 × 10^-4), ferritin (OR = 1.32, 95%CI = 1.07-1.62, p =0.009) and transferrin saturation (OR = 1.14, 95%CI = 1.06-1.23, p = 5.43 × 10^-4) were associated with an increased risk of sepsis. No significant causal relationships between sepsis and other four iron status biomarkers were observed. Conclusions: This present bidirectional MR analysis suggested the causal association of the high iron status with sepsis susceptibility, while the reverse causality hypothesis did not hold. The levels of transferrin, hemoglobin, erythrocytes, and reticulocytes were not significantly associated with sepsis. Further studies will be required to confirm the potential clinical value of such a prevention and treatment strategy.

Cardiovascular health and risk of hospitalization with COVID-19: A Mendelian Randomization study

Background

Susceptibility to and severity of COVID-19 is associated with risk factors for and presence of cardiovascular disease.

Methods

We performed a 2-sample Mendelian randomization to determine whether blood pressure (BP), body mass index (BMI), presence of type 2 diabetes (T2DM) and coronary artery disease (CAD) are causally related to presentation with severe COVID-19. Variant-exposure instrumental variable associations were determined from most recently published genome-wide association and meta-analysis studies (GWAS) with publicly available summary-level GWAS data. Variant-outcome associations were obtained from a recent GWAS meta-analysis of laboratory confirmed diagnosis of COVID-19 with severity determined according to need for hospitalization/death. We also examined reverse causality using exposure as diagnosis of severe COVID-19 causing cardiovascular disease.

Results

We found no evidence for a causal association of cardiovascular risk factors/disease with severe COVID-19 (compared to population controls), nor evidence of reverse causality. Causal odds ratios (OR, by inverse variance weighted regression) for BP (OR for COVID-19 diagnosis 1.00 [95% confidence interval (CI): 0.99-1.01, P = 0.604] per genetically predicted increase in BP) and T2DM (OR for COVID-19 diagnosis to that of genetically predicted T2DM 1.02 [95% CI: 0.9-1.05, P = 0.927], in particular, were close to unity with relatively narrow confidence intervals.

Conclusion

The association between cardiovascular risk factors/disease with that of hospitalization with COVID-19 reported in observational studies could be due to residual confounding by socioeconomic factors and /or those that influence the indication for hospital admission.

Leveraging Genetic Data to Elucidate the Relationship Between COVID-19 and Ischemic Stroke

Background

The relationship between COVID‐19 and ischemic stroke is poorly understood due to potential unmeasured confounding and reverse causation. We aimed to leverage genetic data to triangulate reported associations.

Methods and Results

Analyses primarily focused on critical COVID‐19, defined as hospitalization with COVID‐19 requiring respiratory support or resulting in death. Cross‐trait linkage disequilibrium score regression was used to estimate genetic correlations of critical COVID‐19 with ischemic stroke, other related cardiovascular outcomes, and risk factors common to both COVID‐19 and cardiovascular disease (body mass index, smoking and chronic inflammation, estimated using C‐reactive protein). Mendelian randomization analysis was performed to investigate whether liability to critical COVID‐19 was associated with increased risk of any cardiovascular outcome for which genetic correlation was identified. There was evidence of genetic correlation between critical COVID‐19 and ischemic stroke (r_g=0.29, false discovery rate [FDR]=0.012), body mass index (r_g=0.21, FDR=0.00002), and C‐reactive protein (r_g=0.20, FDR=0.00035), but no other trait investigated. In Mendelian randomization, liability to critical COVID‐19 was associated with increased risk of ischemic stroke (odds ratio [OR] per logOR increase in genetically predicted critical COVID‐19 liability 1.03, 95% CI 1.00–1.06, P‐value=0.03). Similar estimates were obtained for ischemic stroke subtypes. Consistent estimates were also obtained when performing statistical sensitivity analyses more robust to the inclusion of pleiotropic variants, including multivariable Mendelian randomization analyses adjusting for potential genetic confounding through body mass index, smoking, and chronic inflammation. There was no evidence to suggest that genetic liability to ischemic stroke increased the risk of critical COVID‐19.

Conclusions

These data support that liability to critical COVID‐19 is associated with an increased risk of ischemic stroke. The host response predisposing to severe COVID‐19 is likely to increase the risk of ischemic stroke, independent of other potentially mitigating risk factors.