Comorbidities and Susceptibility to COVID-19: A Generalized Gene Set Data Mining Approach

Genetic justification of COVID-19 patient outcomes using DERGA, a novel data ensemble refinement greedy algorithm

Complement inhibition has shown promise in various disorders, including COVID-19. A prediction tool including complement genetic variants is vital. This study aims to identify crucial complement-related variants and determine an optimal pattern for accurate disease outcome prediction. Genetic data from 204 COVID-19 patients hospitalized between April 2020 and April 2021 at three referral centres were analysed using an artificial intelligence-based algorithm to predict disease outcome (ICU vs. non-ICU admission). A recently introduced alpha-index identified the 30 most predictive genetic variants. DERGA algorithm, which employs multiple classification algorithms, determined the optimal pattern of these key variants, resulting in 97% accuracy for predicting disease outcome. Individual variations ranged from 40 to 161 variants per patient, with 977 total variants detected. This study demonstrates the utility of alpha-index in ranking a substantial number of genetic variants. This approach enables the implementation of well-established classification algorithms that effectively determine the relevance of genetic variants in predicting outcomes with high accuracy.

Better safe than sorry-Whole-genome sequencing indicates that missense variants are significant in susceptibility to COVID-19

Undoubtedly, genetic factors play an important role in susceptibility and resistance to COVID-19. In this study, we conducted the GWAS analysis. Out of 15,489,173 SNPs, we identified 18,191 significant SNPs for severe and 11,799 SNPs for resistant phenotype, showing that a great number of loci were significant in different COVID-19 representations. The majority of variants were synonymous (60.56% for severe, 58.46% for resistant phenotype) or located in introns (55.77% for severe, 59.83% for resistant phenotype). We identified the most significant SNPs for a severe outcome (in AJAP1 intron) and for COVID resistance (in FIG4 intron). We found no missense variants with a potential causal function on resistance to COVID-19; however, two missense variants were determined as significant a severe phenotype (in PM20D1 and LRP4 exons). None of the aforementioned SNPs and missense variants found in this study have been previously associated with COVID-19.

Integrative Analysis of Proteome-wide Association Studies and Functional Enrichment Analysis to Identify Genes and Chemicals Associated with Alcohol Dependence

OBJECTIVES

Alcohol dependence accounts for a large proportion of the global burden of disease and disability. This study aims to investigate the candidate genes and chemicals associated with alcohol dependence.

METHODS

Using data from published alcohol dependence genome-wide association studies, we first conducted a proteome-wide association study of alcohol dependence by integrating alcohol dependence genome-wide association studies with 2 human brain reference proteomes of dorsolateral prefrontal cortex from the Religious Order Study and Rush Memory and Aging Project and the Banner Sun Health Research Institute. Then, based on the identified genes in proteome-wide association study, we conducted functional enrichment analysis and chemical-related functional enrichment analysis to detect the related Gene Ontology terms and chemicals.

RESULTS

Proteome-wide association study identified several potential candidate genes for alcohol dependence, such as GOT2 (P = 7.59 × 10-6) and C3orf33 (P = 5.00 × 10-3). Furthermore, functional enrichment analysis identified multiple candidate Gene Ontology terms associated with alcohol dependence, such as glyoxylate metabolic process (adjusted P = 2.99 × 10-6) and oxoglutarate metabolic process (adjusted P = 9.95 × 10-6). Chemical-related functional enrichment analysis detected several alcohol dependence-related candidate chemicals, such as pitavastatin (P = 2.00 × 10-4), cannabinoids (P = 4.00 × 10-4), 11-nor-Δ(9)-tetrahydrocannabinol-9-carboxylic acid (P = 4.00 × 10-4), and gabapentin (P = 2.00 × 10-3).

CONCLUSIONS

Our study reports multiple candidate genes and chemicals associated with alcohol dependence, providing novel clues for understanding the biological mechanism of alcohol dependence.

Effects of mild obesity on outcomes in Japanese patients with COVID-19: a nationwide consortium to investigate COVID-19 host genetics

BACKGROUND

Obesity is reported to be a risk factor for severe disease in patients with coronavirus disease 2019 (COVID-19). However, there are no specific reports on the risk of severe disease according to body mass index (BMI) in Japan. Thus, this study aimed to investigate the effect of obesity stratified by BMI on the severity of COVID-19 in the general Japanese population.

METHODS

From February 2020 to May 2021, 1 837 patients aged ≥18 years were enrolled in the Japan COVID-19 Task Force. Patients with known BMI and disease severity were analyzed. Severity was defined as critical if the patient was treated in the intensive care unit, required invasive mechanical ventilation, or died.

RESULTS

Class 1 obesity (25.0 ≤ BMI < 30.0 kg/m²), class 2 obesity (30.0 ≤ BMI < 35.0 kg/m²), and class 3 or 4 obesity (BMI ≥ 35 kg/m²) were present in 29%, 8%, and 3% of the cases, respectively. Multiple logistic regression analysis with known risk factors for critical illness indicated that class 2 obesity was an independent risk factor for oxygenation (adjusted odds ratio, 4.75) and critical cases (adjusted odds ratio, 1.81). Class 1 obesity and class 3 or 4 obesity were independent risk factors for oxygen administration (adjusted odds ratios 2.01 and 3.12, respectively), but not for critical cases. However, no differences in the mortality rates were observed between the BMI classes (P = 0.5104).

CONCLUSION

Obesity is a risk factor for respiratory failure in Japanese patients with COVID-19, regardless of the degree of obesity. However, it may not cause severe COVID-19 in a dose-response relationship with BMI. COVID-19 patients with mild obesity may benefit from aggressive intensive care.

The Association Between Previous Influenza Vaccination and COVID-19 Infection Risk and Severity: A Systematic Review and Meta-analysis

INTRODUCTION

The association between influenza vaccination and COVID-19 remains controversial. This meta-analysis aimed to investigate whether influenza vaccination reduces the susceptibility and severity of SARS-CoV-2 infection.

METHODS

A systematic literature search of PubMed, Web of Science, the Cochrane Library, Embase, China National Knowledge Infrastructure, SinoMed, Wanfang Data Knowledge Service Platform, and China Science and Technology Journal VIP Database was conducted from database inception to August 2021. The pooled RR with 95% CI was used to estimate the effect of influenza vaccination on COVID-19. The I² value was used to assess heterogeneity. If I²>50%, the random-effects model was used as the pooling method.

RESULTS

A total of 23 published articles with 1,037,445 participants were identified. This meta-analysis showed that influenza vaccination was associated with reduced risk of COVID-19 infection (RR=0.83, 95% CI=0.76, 0.90) and hospitalization (RR=0.71, 95% CI=0.59, 0.84), although not significantly associated with intensive care unit admission and death (risk of intensive care unit admission: RR=0.93, 95% CI=0.64, 1.36; risk of death: RR=0.83, 95% CI=0.68, 1.01). Further analysis suggested that the tetravalent influenza vaccine may be associated with a reduced risk of COVID-19 infection (RR=0.74, 95% CI=0.65, 0.84).

DISCUSSION

The results suggest that influenza vaccination is associated with reduced susceptibility to or disease severity of COVID-19 and that influenza vaccination may reduce the risk of COVID-19 and improve clinical outcomes.

The role of microRNAs in solving COVID-19 puzzle from infection to therapeutics: A mini-review

Nowadays, one of the major global health concerns is coronavirus disease 2019 (COVID-19), which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Even though numerous treatments and vaccines to combat this virus are currently under development, the detailed molecular mechanisms underlying the pathogenesis of this disease are yet to be elucidated to design future therapeutic tools against SARS-CoV-2 variants. MicroRNAs (miRNAs) are small (20-24 nucleotides), non-coding RNA molecules that regulate post-transcriptional gene expression. Recently, it has been demonstrated that both host and viral-encoded miRNAs are crucial for the successful infection of SARS-CoV-2. For instance, dysregulation of miRNAs that modulate multiple genes expressed in COVID-19 patients with comorbidities (e.g., type 2 diabetes, lung adenocarcinoma, and cerebrovascular disorders) could affect the severity of the disease. Thus, altered expression levels of circulating miRNAs might be helpful to diagnose this illness and forecast whether a COVID-19 patient could develop a severe state of the disease. Besides, researchers have found a number of miRNAs could inhibit the expression of proteins, such as ACE2, TMPRSS2, spike, and Nsp12, involved in the life cycle of SARS-CoV-2. Accordingly, miRNAs represent potential biomarkers and therapeutic targets for this devastating viral disease. Therefore, in this current review, we present the recent discoveries regarding the clinical relevance and biological roles of miRNAs in COVID-19.

Cannabis Use among Cancer Survivors amid the COVID-19 Pandemic: Results from the COVID-19 Cannabis Health Study

Clinical indications for medicinal cannabis use include those with cancer, a subgroup advised to avoid exposure to COVID-19. This study aims to identify changes to cannabis use, methods of cannabis delivery, and coping strategies among cancer survivors since the pandemic by cancer status. Chi-squared tests were used for univariate comparisons of demographic characteristics, cannabis use patterns, COVID-19 symptoms, and coping behaviors by cancer survivor status. Data included 158 responses between 21 March 2020 and 23 March 2021, from medicinal cannabis users, categorized as cancer survivors (n = 79) along with age-matched medicinal cannabis users without a history of cancer (n = 79). Compared to adults without a history of cancer, cancer survivors were more likely to report use of cannabis as a way of managing nausea/vomiting (40.5% versus 20.3%, p = 0.006), headaches or migraines (35.4% versus 19.0%, p = 0.020), seizures (8.9% versus 1.3%, p = 0.029), and sleep problems (70.9% versus 54.4%, p = 0.033), or as an appetite stimulant (39.2% versus 17.7%, p = 0.003). Nearly 23% of cancer survivors reported an advanced cannabis supply of more than 3 months compared to 14.3% of adults without a history of cancer (p = 0.002); though the majority of cancer survivors reported less than a one-month supply. No statistically significant differences were observed by cancer survivor status by cannabis dose, delivery, or sharing of electronic vaping devices, joints, or blunts. Cancer survivors were more likely to report a fear of being diagnosed with COVID-19 compared to adults without a history of cancer (58.2% versus 40.5%, p = 0.026). Given the frequency of mental and physical health symptoms reported among cancer survivors, clinicians should consider conversations about cannabis use with their patients, in particular among cancer survivors.