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

Evaluation of COVID-19 vaccines in primary prevention against infections and reduction in severity of illness following the outbreak of SARS-CoV-2 omicron variant in Shanghai

Objectives

To evaluate COVID-19 vaccines in primary prevention against infections and lessen the severity of illness following the most recent outbreak of the SARS-CoV-2 Omicron variant in Shanghai.

Data sources

Data from 153,544 COVID-19 patients admitted to the Shanghai "Four-Leaf Clover" Fangcang makeshift shelter hospital were collected using a structured electronic questionnaire, which was then merged with electronic medical records of the hospital. For healthy controls, data on vaccination status and other information were obtained from 228 community-based residents, using the same structured electronic questionnaire.

Methods

To investigate whether inactivated vaccines were effective in protecting against SARS-CoV-2 virus, we estimated the odds ratio (OR) of the vaccination by comparing cases and matched community-based healthy controls. To evaluate the potential benefits of vaccination in lowering the risk of symptomatic infection (vs. asymptomatic), we estimated the relative risk (RR) of symptomatic infections among diagnosed patients. We also applied multivariate stepwise logistic regression analyses to measure the risk of disease severity (symptomatic vs. asymptomatic and moderate/severe vs. mild) in the COVID-19 patient cohort with vaccination status as an independent variable while controlling for potential confounding factors.

Results

Of the 153,544 COVID-19 patients included in the analysis, the mean age was 41.59 years and 90,830 were males (59.2%). Of the study cohort, 118,124 patients had been vaccinated (76.9%) and 143,225 were asymptomatic patients (93.3%). Of the 10,319 symptomatic patients, 10,031 (97.2%), 281 (2.7%), and 7 (0.1%) experienced mild, moderate, and severe infections, respectively. Hypertension (8.7%) and diabetes (3.0%) accounted for the majority of comorbidities. There is no evidence that the vaccination helped protect from infections (OR = 0.82, p = 0.613). Vaccination, however, offered a small but significant protection against symptomatic infections (RR = 0.92, p < 0.001) and halved the risk of moderate/severe infections (OR = 0.48, 95% CI: 0.37-0.61). Older age (≥60 years) and malignant tumors were significantly associated with moderate/severe infections.

Conclusion

Inactivated COVID-19 vaccines helped provide small but significant protection against symptomatic infections and halved the risk of moderate/severe illness among symptomatic patients. The vaccination was not effective in blocking the SARS-CoV-2 Omicron Variant community spread.

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.

Estimating causal effects of genetically predicted type 2 diabetes on COVID-19 in the East Asian population

BACKGROUND

Observational studies suggested that type 2 diabetes mellitus (T2DM) was associated with an increased risk of coronavirus disease 2019 (COVID-19). However, Mendelian randomization (MR) studies in the European population failed to find causal associations, partly because T2DM was pleiotropically associated with body mass index (BMI). We aimed to estimate the causal effects of T2DM on COVID-19 outcomes in the East Asian (EAS) population using a two-sample MR approach.

METHODS

We obtained summary statistics from a genome-wide association study (GWAS) that included 433,540 EAS participants as the exposure dataset for T2DM risk and from COVID-19 Host Genetics Initiative GWAS meta-analyses (round 7) of EAS ancestry as the outcome dataset for COVID-19 susceptibility (4,459 cases and 36,121 controls), hospitalization (2,882 cases and 31,200 controls), and severity (794 cases and 4,862 controls). As the main MR analysis, we performed the inverse variance weighted (IVW) method. Moreover, we conducted a series of sensitivity analyses, including IVW multivariable MR using summary statistics for BMI from a GWAS with 158,284 Japanese individuals as a covariate.

RESULTS

The IVW method showed that the risk of T2DM significantly increased the risk of COVID-19 susceptibility (odds ratio [OR] per log (OR) increase in T2DM, 1.11; 95% confidence interval [CI], 1.02-1.20; P = 0.014) and hospitalization (OR, 1.15; 95% CI, 1.04-1.26; P = 0.005), although the risk of severity was only suggestive. Moreover, IVW multivariable MR analysis indicated that the causal effects of T2DM on COVID-19 outcomes were independent of the effect of BMI.

CONCLUSIONS

Our MR study indicated for the first time that genetically predicted T2DM is a risk factor for SARS-CoV-2 infection and hospitalized COVID-19 independent of obesity in the EAS population.

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.