The Association of IL-6, TNFα and CRP Gene Polymorphisms with Coronary Artery Disease in a Tunisian Population: A Case-Control study

Impacts of Inflammatory Cytokines Variants on Systemic Inflammatory Profile and COVID-19 Severity

BACKGROUND

Cytokine storm is known to impact the prognosis of coronavirus disease 2019 (COVID-19), since pro-inflammatory cytokine variants are associated with cytokine storm. It is tempting to speculate that pro-inflammatory cytokines variants may impact COVID-19 outcomes by modulating cytokine storm. Here, we verified this hypothesis via a comprehensive analysis.

METHODS

PubMed, Cochrane Library, Central, CINAHL, and ClinicalTrials.gov were searched until December 15, 2023. Case-control or cohort studies that investigated the impacts of rs1800795 or rs1800629 on COVID-19 susceptibility, severity, mortality, IL-6, TNF-α, or CRP levels were included after an anonymous review by two independent reviewers and consultations of disagreement by a third independent reviewer.

RESULTS

47 studies (8305 COVID-19 individuals and 17,846 non-COVID-19 individuals) were analyzed. The rs1800629 A allele (adenine at the -308 position of the promoter was encoded by the A allele) was associated with higher levels of tumor necrosis factor-α (TNF-α) and C-reactive protein (CRP). In contrast, the rs1800795 C allele (cytosine at the -174 position of the promoter was encoded by the C allele) was linked to higher levels of interleukin-6 (IL-6) and CRP. In addition, the A allele of rs1800629 increased the severity and mortality of COVID-19. However, the C allele of rs1800795 only increased COVID-19 susceptibility.

CONCLUSIONS

rs1800629 and rs1800795 variants of pro-inflammatory cytokines have significant impacts on systemic inflammatory profile and COVID-19 clinical outcomes. rs1800629 may serve as a genetic marker for severe COVID-19.

mGWAS-Explorer 2.0: Causal Analysis and Interpretation of Metabolite-Phenotype Associations

Metabolomics-based genome-wide association studies (mGWAS) are key to understanding the genetic regulations of metabolites in complex phenotypes. We previously developed mGWAS-Explorer 1.0 to link single-nucleotide polymorphisms (SNPs), metabolites, genes and phenotypes for hypothesis generation. It has become clear that identifying potential causal relationships between metabolites and phenotypes, as well as providing deep functional insights, are crucial for further downstream applications. Here, we introduce mGWAS-Explorer 2.0 to support the causal analysis between >4000 metabolites and various phenotypes. The results can be interpreted within the context of semantic triples and molecular quantitative trait loci (QTL) data. The underlying R package is released for reproducible analysis. Using two case studies, we demonstrate that mGWAS-Explorer 2.0 is able to detect potential causal relationships between arachidonic acid and Crohn's disease, as well as between glycine and coronary heart disease.