Introduction: Previous observational studies have indicated that gut microbiota and metabolites may contribute to heart failure and its risk factors. However, with the limitation of reverse causality and confounder in observational studies, such relationship remains unclear. This study aims to reveal the causal effect of gut microbiota and metabolites on heart failure and its risk factors.Methods: This study collected summary statistics regarding gut microbiota and metabolites, heart failure, diabetes, hypertension, chronic kidney disease, myocardial infarction, atrial fibrillation, hypertrophic cardiomyopathy, dilated cardiomyopathy, coronary heart disease, valvular heart disease, and myocarditis. Two-sample Mendelian randomization analysis was performed using MR-Egger, inverse variance weighted (IVW), MR-PRESSO, maximum likelihood, and weighted median.Results: Results from gene prediction showed that among all gut microbiota, candida, shigella, and campylobacter were not associated with higher incidence of heart failure. However, genetic prediction suggested that for every 1 unit increase in shigella concentration, the relative risk increased by 38.1% for myocarditis and 13.3% for hypertrophic cardiomyopathy. Besides, for every 1 unit increased in candida concentration, the relative risk of chronic kidney disease increased by 7.1%. As for intestinal metabolites, genetic prediction results suggested that for every 1 unit increase in betaine, the relative risk of heart failure and myocardial infarction increased by 1.4% and 1.7%, separately.Conclusions: This study suggested new evidence of the relationship between gut microbiota and heart failure and its risk factors, which may shed light on designing microbiome- and microbiome-dependent metabolite interventions on heart failure and its risk factors in clinical trials in the future.

Background: Women suffer from various distress and disturbances after menopause, including osteoporosis, a risk factor associated with multiple diseases. Altered gut microbiota has been implicated in postmenopausal osteoporosis. In this study, to understand gut microbiota signatures and fecal metabolite changes in postmenopausal women with osteoporosis, 108 postmenopausal women were recruited for intestinal microbiota and fecal metabolite detection. Among these participants, 98 patients, who met the inclusion criteria, were divided into postmenopausal osteoporosis (PMO) and non-postmenopausal osteoporosis (non-PMO) groups based on bone mineral density (BMD). The compositions of gut bacteria and fungi were examined by 16 S rRNA gene sequencing and ITS sequencing, respectively. Meanwhile, fecal metabolites were analyzed using liquid chromatography coupled with mass spectrometry (LC-MS). Results: We found that bacterial α-diversity and β-diversity were significantly altered in PMO compared to non-PMO patients. Interestingly, fungi composition showed larger changes, and the differences in β-diversity were more significant between PMO and non-PMO patients. Metabolomics analysis revealed that fecal metabolites, such as levulinic acid, N-Acetylneuraminic acid, and the corresponding signaling pathways were also changed significantly, especially in the alpha-Linolenic acid metabolism and selenocompound metabolism. The screened differential bacteria, fungi, and metabolites closely correlated with clinical findings between these two groups, for example, the bacterial genus, Fusobacterium, the fungal genus, Devriesia, and the metabolite, L-pipecolic acid, were significantly associated with BMD. Conclusions: Our findings indicated that there were remarkable changes in gut bacteria, fungi, and fecal metabolites in postmenopausal women, and such changes were notably correlated with patients’ BMD ​​and clinical findings. These correlations provide novel insights into the mechanism of PMO development, potential early diagnostic indicators, and new therapeutic approaches to improve bone health in postmenopausal women.

Background: Accumulating evidence suggests that alterations in gut microbiota composition and diversity are associated with Atopic dermatitis (AD). But until now, the causal association between them has been unclear.Methods: We employed a two-sample Mendelian Randomization (MR) study to estimate the potential causality of gut microbiota on AD risk. The summary statistics related to the gut microbiota were obtained from a large-scale genome-wide genotype and 16S fecal microbiome dataset from 18,340 individuals (24 cohorts) analyzed by the MiBioGen Consortium, comprising 211 gut microbiota. AD data were also derived from strictly defined AD data collected by FinnGen biobank analysis, which included 218,467 European ancestors (5,321 AD patients and 213,146 controls). The inverse variance weighted method (IVW), weighted median (WME), and MR-Egger were used to determine the changes of AD pathogenic bacterial taxa, followed by sensitivity analysis including horizontal pleiotropy analysis, Cochran’s Q test, and the leave-one-out method to assess the reliability of the results. In addition, MR Steiger’s test was used to test the suppositional relationship between exposure and outcome.Results: A total of 2,289 SNPs (p < 1 × 10⁻⁵) were included, including 5 taxa and 17 bacterial characteristics (1 phylum, 3 classes, 1 order, 4 families, and 8 genera), after excluding the IVs with linkage disequilibrium (LD). Combining the analysis of the results of the IVW models, there were 6 biological taxa (2 families, and 4 genera) of the intestinal flora positively associated with the risk of AD and 7 biological taxa (1 phylum, 2 classes, 1 order, 1 family, and 2 genera) of the intestinal flora negatively associated. The IVW analysis results showed that Tenericutes, Mollicutes, Clostridia, Bifidobacteriaceae, Bifidobacteriales, Bifidobacterium, and Christensenellaceae R 7 group were negatively correlated with the risk of AD, while Clostridiaceae 1, Bacteroidaceae, Bacteroides, Anaerotruncus, the unknown genus, and Lachnospiraceae UCG001 showed the opposite trend. And the results of the sensitivity analysis were robust. MR Steiger’s test showed a potential causal relationship between the above intestinal flora and AD, but not vice versa.Conclusion: The present MR analysis genetically suggests a causal relationship between changes in the abundance of the gut microbiota and AD risk, thus not only providing support for gut microecological therapy of AD but also laying the groundwork for further exploration of the mechanisms by which the gut microbiota contributes to the pathogenesis of AD.

Objective: Previous studies have demonstrated that trimethylamine N-oxide (TMAO) and its precursors, including choline, betaine, and carnitine, are closely associated with blood pressure (BP) changes. Nevertheless, with the limitation of reverse causality and confounder in observational studies, such a relationship remains unclear. We aimed to assess the causal relationship of TMAO and its precursors with BP by the Mendelian Randomization (MR) approach.Method: In this study, two-sample MR was used to reveal the causal effect of TMAO and its precursors on BP. Pooled data of TMAO and its precursors was from genome-wide association studies (GWAS) which includes summary data of human metabolome in 2,076 European participants from Framingham Heart Study. Summary-level data for BP was extracted from the International Consortium of Blood Pressure-Genome Wide Association Studies. Inverse variance weighted (IVW), MR Egger regression, Maximum likelihood, Weighted median, and MR pleiotropy residual sum and outlier test (MR-PRESSO) were used in this MR analysis.Results: A total of 160 independent SNP loci were associated with TMAO and three precursors, including 58 associated with TMAO, 29 associated with choline, 44 associated with betaine, and 29 associated with carnitine, were selected. MR results suggested that a 1 unit increase in TMAO should be associated with a 1SD increase in systolic BP mmHg (beta: 0.039, SE, 0.072, p = 0.020). Additionally, our findings also indicated that a 1 unit increase in carnitine should be associated with a 1SD increase in systolic BP mmHg (beta: 0.055, SE: 0.075, p = 0.039). This result was also confirmed by sensitivity analysis methods such as Maximum likelihood, MR-PRESSO, and Weighted median. No effects of betaine or choline on systolic or diastolic BP were observed in the present study.Conclusion: Our study provides evidence of a causal relationship of TMAO and its precursors with BP, suggesting that mediating the generation of TMAO would be beneficial for lowering BP.

Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the most malignant tumors and approximately 5% of patients with chronic pancreatitis (CP) inevitably develop PDAC. This study aims explore the key gene regulation involved in the progression of CP to PDAC, with a particular emphasis on the function of lncRNAs. Results: A total of 103 pancreatic tissue samples collected from 11 to 92 patients with CP and PDAC, respectively, were included in this study. After normalizing and logarithmically converting the original data, differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEGs) in each dataset were selected. To determine the main functional pathways of differential mRNAs, we further annotated DEGs using gene ontology (GO) and analyzed the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. In addition, the interaction between lncRNA-miRNA-mRNA was clarified and the protein–protein interaction (PPI) network was constructed to screen for key modules and determine hub genes. Finally, quantitative real-time polymerase chain reaction (qPCR) was used to detect the changes in non-coding RNAs and key mRNAs in the pancreatic tissues of patients with CP and PDAC. In this study, 230 lncRNAs and 17,668 mRNAs were included. There were nine upregulated lncRNAs and 188 downregulated lncRNAs. Furthermore, 2334 upregulated differential mRNAs and 10,341 downregulated differential mRNAs were included in the enrichment analysis. From the KEGG enrichment analysis, cytokine–cytokine receptor interaction, calcium signaling pathway, cAMP signaling pathway, and nicotine addiction exhibited significant differences. Additionally, a total of 52 lncRNAs, 104 miRNAs, and 312 mRNAs were included in the construction of a potential lncRNA-miRNA-mRNA regulatory network. PPI network was established and two of the five central DEGs were created in this module, suggesting that lysophosphatidic acid receptor 1 (LPAR1) and regulator of calcineurin 2 (RCAN2) may play significant roles in the progression from CP to PDAC. Finally, the PCR results suggested that LINC01547/hsa-miR-4694-3p/LPAR1 and LINC00482/hsa-miR-6756-3p/RCAN2 play important roles in the carcinogenesis process of CP. Conclusion: Two signaling axes critical in the progression of CP to PDAC were screened out. Our findings will be useful for novel insights into the molecular mechanism and potential diagnostic or therapeutic biomarkers for CP and PDAC.