Genetically Predicted Peripheral Immune Cells Mediate the Effect of Gut Microbiota on Influenza Susceptibility

The predictive, preventive, and personalized medicine of depression: gut microbiota and inflammation

BACKGROUND

Gut microbiota (GM) is closely associated with the onset of depression, in which inflammation plays an essential role. Identifying specific GM associated with depression and their mechanisms, based on the principles of predictive, preventive, and personalized medicine (PPPM), is a critical step toward achieving targeted prevention and personalized treatment for depression.

WORKING HYPOTHESIS AND METHODOLOGY

We hypothesized that both gut microbiota (GM) and cytokines influence the onset of depression, with cytokines acting as mediators of GM effects on depression. To test this hypothesis, we employed univariate Mendelian Randomization (UVMR) analysis to identify GM taxa associated with depression and cytokines and to determine the potential role of the identified GM taxa on these cytokines. Subsequently, multivariate Mendelian randomization (MVMR) was used to infer the mediating role of cytokines between the identified differential genus of GM and depression. Our results indicate that immune imbalance due to intestinal dysbiosis serves as an early risk indicator for the onset of depression. This provides a basis for utilizing non-invasive stool detection of GM for early screening, timely prevention, and personalized treatment of depression. By combining non-invasive stool detection of GM with existing methods, such as psychological questionnaires, we can jointly predict and assess the risk of developing depression. Additionally, formulating personalized treatment protocols that combine probiotics and medication can help transition depression management from reactive medicine to predictive, preventive, and personalized medicine (PPPM).

RESULTS

UVMR identified 15 GM taxa and 4 cytokines associated with the onset of depression. Specifically, Romboutsia, Intestinimonas, Ruminococcaceae UCG011, and circulating ADA, IL-18R1 were all inferred to be protective factors against the onset of depression. Conversely, Lachnospiraceae FCS020 group, Streptococcus, Marvinbryantia, VEGF_A, and TNFSF14 were inferred as risk factors for the onset of depression. Further, MVMR validated the mediating role of some cytokines in the effects of GM on depression.

CONCLUSIONS

Our study highlights the influence of alterations in GM on depression, revealing a mediating role of inflammation. By regulating these specific GM, it is hoped that the clinical treatment of depression can be transformed from traditional medicine to PPPM. With the help of mendelian randomization (MR) method, this study provides support for the wide application of non-invasive stool detection of GM for early screening of depression in clinical and carries out precise treatment based on the screening results, targeting the supplementation of specific bacteria, correcting the immune imbalance to prevent depression, and mitigating or blocking the disease process of depression.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13167-024-00379-z.

Assessing the causal relationship between metabolic biomarkers and coronary artery disease by Mendelian randomization studies

The development of coronary artery disease (CAD) is significantly affected by impaired endocrine and metabolic status. Under this circumstance, improved prevention and treatment of CAD may result from knowing the connection between metabolites and CAD. This study aims to delve into the causal relationship between human metabolic biomarkers and CAD by using two-sample Mendelian randomization (MR). Utilizing two-sample bidirectional MR analysis, we assessed the correlation between 1400 blood metabolites and CAD, and the metabolites data from the CLSA, encompassing 8299 participants. Metabolite analysis identified 1091 plasma metabolites and 309 ratios as instrumental variables. To evaluate the causal link between metabolites and CAD, we analyzed three datasets: ebi-a-GCST005195 (547,261 European & East Asian samples), bbj-a-159 (29,319 East Asian CAD cases & 183,134 East Asian controls), and ebi-a-GCST005194 (296,525 European & East Asian samples). To estimate causal links, we utilized the IVW method. To conduct sensitivity analysis, we used MR-Egger, Weighted Median, and MR-PRESSO. Additionally, we employed MR-Egger interception and Cochran's Q statistic to assess potential heterogeneity and pleiotropy. What's more, replication and reverse analyses were performed to verify the reliability of the results and the causal order between metabolites and disease. Furthermore, we conducted a pathway analysis to identify potential metabolic pathways. 59 blood metabolites and 27 metabolite ratios nominally associated with CAD (P < 0.05) were identified by IVW analysis method. A total of four known blood metabolites, namely beta-hydroxyisovaleroylcarnitine (OR 1.06, 95% CI 1.027-1.094, FDR 0.07), 1-palmitoyl-2-arachidonoyl (OR 1.07, 95% CI 1.029-1.110, FDR 0.09), 1-stearoyl-2- docosahexaenoyl (OR 1.07, 95% CI 1.034-1.113, FDR 0.07) and Linoleoyl-arachidonoyl-glycerol, (OR 1.07, 95% CI 1.036-1.105, FDR 0.05), and two metabolite ratios, namely spermidine to N-acetylputrescine ratio (OR 0.94, 95% CI 0.903-0.972, FDR 0.09) and benzoate to linoleoyl-arachidonoyl-glycerol ratio (OR 0.87, 95% CI 0.879-0.962, FDR 0.07), were confirmed as having a significant causal relationship with CAD, after correcting for the FDR method (p < 0. 1). A causal relationship was found to be established between beta -hydroxyisovalerylcarnitine and CAD with the validation in other two datasets. Moreover, multiple metabolic pathways were discovered to be associated with CAD. Our study supports the hypothesis that metabolites have an impact on CAD by demonstrating a causal relationship between human metabolites and CAD. This study is important for new strategies for the prevention and treatment of CAD.