The causal links between gut microbiota and COVID‐19: A Mendelian randomization study

Song, Jukun; Wu, Yadong; Yin, Xinhai; Ma, Hong; Zhang, Junmei

doi:10.1002/jmv.28784

Cited by 22 publications

(14 citation statements)

References 45 publications

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“…Its potential to produce specific metabolites has not been fully studied. At present, class.Negativicutes is closely related to diseases such as COVID-19 ( Song et al., 2023 ), low birthweight infants ( Warner et al., 2016 ), and obesity ( Hu et al., 2022 ). In this study, it was found that there was a significant causal relationship between class.Negativicutes and insomnia.…”

Section: Discussionmentioning

confidence: 99%

Causal relationship between the gut microbiota and insomnia: a two-sample Mendelian randomization study

Wang,

Gao,

Zhang

et al. 2024

Front. Cell. Infect. Microbiol.

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BackgroundChanges in the gut microbiota are closely related to insomnia, but the causal relationship between them is not yet clear.ObjectiveTo clarify the relationship between the gut microbiota and insomnia and provide genetic evidence for them, we conducted a two-sample Mendelian randomization study.MethodsWe used a Mendelian randomized two-way validation method to discuss the causal relationship. First, we downloaded the data of 462,341 participants relating to insomnia, and the data of 18,340 participants relating to the gut microbiota from a genome-wide association study (GWAS). Then, we used two regression models, inverse-variance weighted (IVW) and MR-Egger regression, to evaluate the relationship between exposure factors and outcomes. Finally, we took a reverse MR analysis to assess the possibility of reverse causality.ResultsThe combined results show 19 gut microbiotas to have a causal relationship with insomnia (odds ratio (OR): 1.03; 95% confidence interval (CI): 1.01, 1.05; p=0.000 for class. Negativicutes; OR: 1.03; 95% CI: 1.01, 1.05; p=0.000 for order.Selenomonadales; OR: 1.01; 95% CI: 1.00, 1.02; p=0.003 for genus.RikenellaceaeRC9gutgroup). The results were consistent with sensitivity analyses for these bacterial traits. In reverse MR analysis, we found no statistical difference between insomnia and these gut microbiotas.ConclusionThis study can provide a new direction for the causal relationship between the gut microbiota (class.Negativicutes, order.Selenomonadales, genus.Lactococcus) and insomnia and the treatment or prevention strategies of insomnia.

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Section: Discussionmentioning

confidence: 99%

Causal relationship between the gut microbiota and insomnia: a two-sample Mendelian randomization study

Wang,

Gao,

Zhang

et al. 2024

Front. Cell. Infect. Microbiol.

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“…The IVs were chosen based on the following criteria: [ 1 ] The number of single nucleotide polymorphisms (SNPs) was too small when the candidate SNPs were filtered with the genome-wide significance threshold ( p < 5 × 10^-8) and thus might result in missing potential findings. In this study, locus-wide significance threshold ( p < 1 × 10 − 5) was used to select the potential SNPs associated with GM; [ 2 , 24 , 25 ] To avoid linkage disequilibrium, the SNPs were only retained after the clumping process (r 2 < 0.001 and window size = 10,000 kb); [ 3 ] Proxy SNPs with linkage disequilibrium R 2 > 0.8 were found to substitute the selected SNPs, which were not matched in GWAS of DN; [ 4 ] the SNPs were removed with a minor allele frequency (MAF) less than 0.01; [ 5 ] The strength of each SNP was measured by the F-statistics which was calculated by the following formula: , R 2 was the proportion of the variability of bacterial taxa explained by each SNP and N was the sample size [ 26 ]. For eliminating weak IVs, only those SNPs with F-statistics greater than 10 were kept; [ 6 , 27 ] To avoid the confounders related to SNPs affected DN, PhenoScanner V2, a database of human genotype-phenotype associations was used to recognize and weed out those SNPs linked to the confounding factors (hypertension, autoimmune disease, etc.…”

Section: Methodsmentioning

confidence: 99%

Causal effect of gut microbiota and diabetic nephropathy: a Mendelian randomization study

He,

Chen,

Hao

et al. 2024

Diabetol Metab Syndr

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Background The interaction of dysbiosis of gut microbiota (GM) with diabetic nephropathy (DN) drew our attention and a better understanding of GM on DN might provide potential therapeutic approaches. However, the exact causal effect of GM on DN remains unknown. Methods We applied two-sample Mendelian Randomization (MR) analysis, including inverse variance weighted (IVW), MR-Egger methods, etc., to screen the significant bacterial taxa based on the GWAS data. Sensitivity analysis was conducted to assess the robustness of MR results. To identify the most critical factor on DN, Mendelian randomization-Bayesian model averaging (MR-BMA) method was utilized. Then, whether the reverse causality existed was verified by reverse MR analysis. Finally, transcriptome MR analysis was performed to investigate the possible mechanism of GM on DN. Results At locus-wide significance levels, the results of IVW suggested that order Bacteroidales (odds ratio (OR) = 1.412, 95% confidence interval (CI): 1.025–1.945, P = 0.035), genus Akkermansia (OR = 1.449, 95% CI: 1.120–1.875, P = 0.005), genus Coprococcus 1 (OR = 1.328, 95% CI: 1.066–1.793, P = 0.015), genus Marvinbryantia (OR = 1.353, 95% CI: 1.037–1.777, P = 0.030) and genus Parasutterella (OR = 1.276, 95% CI: 1.022–1.593, P = 0.032) were risk factors for DN. Reversely, genus Eubacterium ventriosum (OR = 0.756, 95% CI: 0.594–0.963, P = 0.023), genus Ruminococcus gauvreauii (OR = 0.663, 95% CI: 0.506–0.870, P = 0.003) and genus Erysipelotrichaceae (UCG003) (OR = 0.801, 95% CI: 0.644–0.997, P = 0.047) were negatively associated with the risk of DN. Among these taxa, genus Ruminococcus gauvreauii played a crucial role in DN. No significant heterogeneity or pleiotropy in the MR result was found. Mapped genes (FDR < 0.05) related to GM had causal effects on DN, while FCGR2B and VNN2 might be potential therapeutic targets. Conclusions This work provided new evidence for the causal effect of GM on DN occurrence and potential biomarkers for DN. The significant bacterial taxa in our study provided new insights for the ‘gut-kidney’ axis, as well as unconventional prevention and treatment strategies for DN.

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“…The estimates are expressed as odds ratios (ORs) with 95% confidence intervals (ci), which indicate the average change in outcomes resulting from each exposure. In this study, IVW method was used as the main analysis method, and other methods were used as auxiliary proof [ 21 ]. These results only provide evidence of a causal relationship between exposure and outcomes, with no other explanation.…”

Section: Methodsmentioning

confidence: 99%

Interaction of immune cells with renal cancer development: Mendelian randomization (MR) study

Lu,

Yin,

Rao

et al. 2024

BMC Cancer

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Background Renal cell carcinoma (RCC) is a prevalent and extensively immune-infiltrated malignancy of the urinary system. Immune cells play a crucial role in both the progression and therapeutic interventions targeting RCC. Nevertheless, the interplay between RCC and immune cells remains understudied, lacking substantial evidence supporting their causal relationship. Methods For the purpose of investigating the causal connection between RCC and immune cell characteristics, a two-way two-sample Mendelian randomization (MR) analysis was carried out in this study. The aim was to determine whether specific immune cell traits have a causal impact on the risk of RCC. In order to achieve this, publicly accessible genetic data was utilized to examine and establish the potential relationship between 731 immune cell characteristics and the likelihood of developing RCC. Additionally, various techniques were applied to verify the reliability, variability, and presence of horizontal pleiotropy in the outcomes. Results We found a bidirectional causal relationship between RCC and immune cells according to the MR analysis results. It should be noted that CD4-CD8-T cells (OR = 1.61, 95%CI = 1.02–2.55, P = 4.07 × 10–2) pose a risk for RCC, whereas BAFF-R (OR = 0.69, 95%CI = 0.53–0.89, P = 5.74 × 10–3) and CD19 (OR = 0.59, 95%CI = 1.02–2.55, P = 4.07 × 10–2) on B cells act as protective factors. Furthermore, the presence of RCC reduces the levels of B cells (OR = 1.05, 95%CI = 1.01–1.09, P = 1.19 × 10–2) and CD8 + T cells (OR = 1.04, 95%CI = 1.00–1.08, P = 2.83 × 10–2). Conclusions Our research illustrates the intricate correlation between immune cells and RCC, presenting novel insights for the prospective safeguarding against RCC risk and the exploration of fresh therapeutic targets.

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The causal links between gut microbiota and COVID‐19: A Mendelian randomization study

Cited by 22 publications

References 45 publications

Causal relationship between the gut microbiota and insomnia: a two-sample Mendelian randomization study

Causal relationship between the gut microbiota and insomnia: a two-sample Mendelian randomization study

Causal effect of gut microbiota and diabetic nephropathy: a Mendelian randomization study

Interaction of immune cells with renal cancer development: Mendelian randomization (MR) study

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