Impact of gut microbiome on dyslipidemia in japanese adults: Assessment of the Shika-machi super preventive health examination results for causal inference

Diet and the gut microbiota profiles in individuals at risk of chronic heart failure - A review on the Asian population

BACKGROUND AND OBJECTIVES

Chronic Heart Failure (CHF) is one of the leading cardiovascular diseases (CVDs), particularly in the Asian population. Individuals with specific health risks, such as obesity, type 2 diabetes, hypertension, dyslipidemia, and coronary artery disease (CAD), are more susceptible to developing CHF. Current evidence is limited to understanding the link between gut microbiota dysbiosis and CHF. Therefore, this review aims to explore the potential connection between dietary patterns, gut microbiota, and its metabolites in individuals at risk of CHF in the Asian population.

METHODS AND STUDY DESIGN

A literature review of cross-sectional studies was conducted using primary keywords such as "Asian", "obesity", "type 2 diabetes", "hypertension", "dyslipidemia", "coronary artery disease", and "chronic heart failure". There was no restriction on sample size.

RESULTS

Several gut microbiotas were found to correlate with CHF risk factors. There were increased levels of Prevotella, Klebsiella, Romboutsia, Catenibacterium, Clostridium, Holdemanella, Ruminococcus, Coprococcus, Parabacteroides, Bacteroides, Lachnoclostridium, Streptococcus, and Megamonas, while decreased levels of Oscillibacter, Bifidobacterium, Lactobacillus, Akkermansia, Roseburia, Faecalibacterium, Pseudobutyrivibrio, and Eubacterium were reported. These microbiota shifts were linked to increased TMAO production and impaired short-chain fatty acids (SCFAs) production. Dietary intake and microbial metabolites were also identified as contributors to the gut microbiota associated with CHF.

CONCLUSIONS

A potential link exists between the gut microbiota profile and CHF risk factors, possibly mediated by microbial metabolites. Dietary patterns may influence CHF-associated gut microbiota and me-tabolites. Future research is needed to investigate how dietary modifications can modulate gut microbiota and its metabolites in CHF patients.

Clade-specific extracellular vesicles from Akkermansia muciniphila mediate competitive colonization via direct inhibition and immune stimulation

Akkermansia muciniphila, a promising candidate for next-generation probiotics, exhibits significant genomic diversity, classified into several distinct clades (AmI to AmIV). Notably, a single Akkermansia clade tends to predominate within individual hosts, with co-occurrence of different clades being rare. The mechanisms driving such clade-specific exclusion remain unclear. Here, we show that extracellular vesicles (EVs) derived from AmII clade inhibit the growth of clade I (AmI), conferring a competitive advantage to AmII. Moreover, we observe clade-specific immunoglobulin A (IgA) responses, where AmII clade-specific IgAs, induced by EVs from AmII, facilitate niche occupancy and competitive exclusion of AmI. These findings provide insights into the competitive dynamics of A. muciniphila clades and suggest that future personalized microbiome interventions could be optimized by considering the clade composition of A. muciniphila in individual hosts.

Characterization of the gut microbiota in polycystic ovary syndrome with dyslipidemia

BACKGROUND

Polycystic ovary syndrome (PCOS) is an endocrinopathy in childbearing-age females which can cause many complications, such as diabetes, obesity, and dyslipidemia. The metabolic disorders in patients with PCOS were linked to gut microbial dysbiosis. However, the correlation between the gut microbial community and dyslipidemia in PCOS remains unillustrated. Our study elucidated the different gut microbiota in patients with PCOS and dyslipidemia (PCOS.D) compared to those with only PCOS and healthy women.

RESULTS

In total, 18 patients with PCOS, 16 healthy females, and 18 patients with PCOS.D were enrolled. The 16 S rRNA sequencing in V3-V4 region was utilized for identifying the gut microbiota, which analyzes species annotation, community diversity, and community functions. Our results showed that the β diversity of gut microbiota did not differ significantly among the three groups. Regarding gut microbiota dysbiosis, patients with PCOS showed a decreased abundance of Proteobacteria, and patients with PCOS.D showed an increased abundance of Bacteroidota compared to other groups. With respect to the gut microbial imbalance at genus level, the PCOS.D group showed a higher abundance of Clostridium_sensu_stricto_1 compared to other two groups. Furthermore, the abundances of Faecalibacterium and Holdemanella were lower in the PCOS.D than those in the PCOS group. Several genera, including Faecalibacterium and Holdemanella, were negatively correlated with the lipid profiles. Pseudomonas was negatively correlated with luteinizing hormone levels. Using PICRUSt analysis, the gut microbiota community functions suggested that certain metabolic pathways (e.g., amino acids, glycolysis, and lipid) were altered in PCOS.D patients as compared to those in PCOS patients.

CONCLUSIONS

The gut microbiota characterizations in patients with PCOS.D differ from those in patients with PCOS and controls, and those might also be related to clinical parameters. This may have the potential to become an alternative therapy to regulate the clinical lipid levels of patients with PCOS in the future.

Genetically predicted gut microbiota mediate the association between plasma lipidomics and primary sclerosing cholangitis

BACKGROUND

Primary sclerosing cholangitis (PSC) is a complex disease with pathogenic mechanisms that remain to be elucidated. Previous observational studies with small sample sizes have reported associations between PSC, dyslipidemia, and gut microbiota dysbiosis. However, the causality of these associations is uncertain, and there has been no systematic analysis to date.

METHODS

The datasets comprise data on PSC, 179 lipid species, and 412 gut microbiota species. PSC data (n = 14,890) were sourced from the International PSC Study Group, while the dataset pertaining to plasma lipidomics originated from a study involving 7174 Finnish individuals. Data on gut microbiota species were derived from the Dutch Microbiome Project study, which conducted a genome-wide association study involving 7738 participants. Furthermore, we employed a two-step Mendelian randomization (MR) analysis to quantify the proportion of the effect of gut microbiota-mediated lipidomics on PSC.

RESULTS

Following a rigorous screening process, our MR analysis revealed a causal relationship between higher levels of gene-predicted Phosphatidylcholine (O-16:1_18:1) (PC O-16:1_18:1) and an increased risk of developing PSC (inverse variance-weighted method, odds ratio (OR) 1.30, 95% confidence interval (CI) 1.03-1.63). There is insufficient evidence to suggest that gene-predicted PSC impacts the levels of PC O-16:1_18:1 (OR 1.01, 95% CI 0.98-1.05). When incorporating gut microbiota data into the analysis, we found that Eubacterium rectale-mediated genetic prediction explains 17.59% of the variance in PC O-16:1_18:1 levels.

CONCLUSION

Our study revealed a causal association between PC O-16:1_18:1 levels and PSC, with a minor portion of the effect mediated by Eubacterium rectale. This study aims to further explore the pathogenesis of PSC and identify promising therapeutic targets. For patients with PSC who lack effective treatment options, the results are encouraging.

Prediction and causal inference of hyperuricemia using gut microbiota

Hyperuricemia (HUA) is a symptom of high blood uric acid (UA) levels, which causes disorders such as gout and renal urinary calculus. Prolonged HUA is often associated with hypertension, atherosclerosis, diabetes mellitus, and chronic kidney disease. Studies have shown that gut microbiota (GM) affect these chronic diseases. This study aimed to determine the relationship between HUA and GM. The microbiome of 224 men and 254 women aged 40 years was analyzed through next-generation sequencing and machine learning. We obtained GM data through 16S rRNA-based sequencing of the fecal samples, finding that alpha-diversity by Shannon index was significantly low in the HUA group. Linear discriminant effect size analysis detected a high abundance of the genera Collinsella and Faecalibacterium in the HUA and non-HUA groups. Based on light gradient boosting machine learning, we propose that HUA can be predicted with high AUC using four clinical characteristics and the relative abundance of nine bacterial genera, including Collinsella and Dorea. In addition, analysis of causal relationships using a direct linear non-Gaussian acyclic model indicated a positive effect of the relative abundance of the genus Collinsella on blood UA levels. Our results suggest abundant Collinsella in the gut can increase blood UA levels.

Causality of the gut microbiome and atherosclerosis-related lipids: a bidirectional Mendelian Randomization study

AIMS

Recent studies have indicated an association between intestinal flora and lipids. However, observational studies cannot indicate causality. In this study, we aimed to investigate the potentially causal relationships between the intestinal flora and blood lipids.

METHODS

We performed a bidirectional two-sample Mendelian Randomization (MR) analysis to investigate the causal relationship between intestinal flora and blood lipids. Summary statistics of genome-wide association studies (GWASs) for the 211 intestinal flora and blood lipid traits (n = 5) were obtained from public datasets. Five recognized MR methods were applied to assess the causal relationship with lipids, among which, the inverse-variance weighted (IVW) regression was used as the primary MR method. A series of sensitivity analyses were performed to test the robustness of the causal estimates.

RESULTS

The results indicated a potential causal association between 19 intestinal flora and dyslipidemia in humans. Genus Ruminococcaceae, Christensenellaceae, Parasutterella, Terrisporobacter, Parabacteroides, Class Erysipelotrichia, Family Erysipelotrichaceae, and order Erysipelotrichales were associated with higher dyslipidemia, whereas genus Oscillospira, Peptococcus, Ruminococcaceae UCG010, Ruminococcaceae UCG011, Dorea, and Family Desulfovibrionaceae were associated with lower dyslipidemia. After using the Bonferroni method for multiple testing correction, Only Desulfovibrionaceae [Estimate = -0.0418, 95% confidence interval [CI]: 0.9362-0.9826, P = 0.0007] exhibited stable and significant negative associations with ApoB levels. The inverse MR analysis did not find a significant causal effect of lipids on the intestinal flora. Additionally, no significant heterogeneity or horizontal pleiotropy for IVs was observed in the analysis.

CONCLUSION

The study suggested a causal relationship between intestinal flora and dyslipidemia. These findings will provide a meaningful reference to discover dyslipidemia for intervention to address the problems in the clinic.

Variations in the Relative Abundance of Gut Bacteria Correlate with Lipid Profiles in Healthy Adults

The gut microbiome is a versatile system regulating numerous aspects of host metabolism. Among other traits, variations in the composition of gut microbial communities are related to blood lipid patterns and hyperlipidaemia, yet inconsistent association patterns exist. This study aims to assess the relationships between the composition of the gut microbiome and variations in lipid profiles among healthy adults. This study used data and samples from 23 adult participants of a previously conducted dietary intervention study. Circulating lipid measurements and whole-metagenome sequences of the gut microbiome were derived from 180 blood and faecal samples collected from eight visits distributed across an 11-week study. Lipid-related variables explained approximately 4.5% of the variation in gut microbiome compositions, with higher effects observed for total cholesterol and high-density lipoproteins. Species from the genera Odoribacter, Anaerostipes, and Parabacteroides correlated with increased serum lipid levels, whereas probiotic species like Akkermansia muciniphila were more abundant among participants with healthier blood lipid profiles. An inverse correlation with serum cholesterol was also observed for Massilistercora timonensis, a player in regulating lipid turnover. The observed correlation patterns add to the growing evidence supporting the role of the gut microbiome as an essential regulator of host lipid metabolism.

Impact of gut microbiome on serum IgG4 levels in the general population: Shika-machi super preventive health examination results

Introduction

Immunoglobulin G4 (IgG4) is a member of the human immunoglobulin G (IgG) subclass, a protein involved in immunity to pathogens and the body's resistance system. IgG4-related diseases (IgG4-RD) are intractable diseases in which IgG4 levels in the blood are elevated, causing inflammation in organs such as the liver, pancreas, and salivary glands. IgG4-RD are known to be more prevalent in males than in females, but the etiology remains to be elucidated. This study was conducted to investigate the relationship between gut microbiota (GM) and serum IgG4 levels in the general population.

Methods

In this study, the relationship between IgG4 levels and GM evaluated in male and female groups of the general population using causal inference. The study included 191 men and 207 women aged 40 years or older from Shika-machi, Ishikawa. GM DNA was analyzed for the 16S rRNA gene sequence using next-generation sequencing. Participants were bifurcated into high and low IgG4 groups, depending on median serum IgG4 levels.

Results

ANCOVA, Tukey's HSD, linear discriminant analysis effect size, least absolute shrinkage and selection operator logistic regression model, and correlation analysis revealed that Anaerostipes, Lachnospiraceae, Megasphaera, and [Eubacterium] hallii group were associated with IgG4 levels in women, while Megasphaera, [Eubacterium] hallii group, Faecalibacterium, Ruminococcus.1, and Romboutsia were associated with IgG4 levels in men. Linear non-Gaussian acyclic model indicated three genera, Megasphaera, [Eubacterium] hallii group, and Anaerostipes, and showed a presumed causal association with IgG4 levels in women.

Discussion

This differential impact of the GM on IgG4 levels based on sex is a novel and intriguing finding.

Impact of gut microbiome on the renin-aldosterone system: Shika-machi Super Preventive Health Examination results

The renin-angiotensin-aldosterone system (RAAS) is a regulatory mechanism of the endocrine system and is associated with various diseases, including hypertension and renal and cardiovascular diseases. The gut microbiota (GM) have been associated with various diseases, mainly in animal models. However, to our knowledge, no studies have examined the relationship between the RAAS and GM in humans. The present study aimed to assess the association between the systemic RAAS and GM genera and their causal relationships. The study participants were 377 members of the general population aged 40 years or older in Shika-machi, Japan. Plasma renin activity (PRA), plasma aldosterone concentration (PAC), aldosterone-renin ratio (ARR), and GM composition were analyzed using the 16S rRNA method. The participants were divided into high and low groups according to the PRA, PAC, and ARR values. U-tests, one-way analysis of covariance, and linear discriminant analysis of effect size were used to identify the important bacterial genera between the two groups, and binary classification modeling using Random Forest was used to calculate the importance of the features. The results showed that Blautia, Bacteroides, Akkermansia, and Bifidobacterium were associated with the RAAS parameters. Causal inference analysis using the linear non-Gaussian acyclic model revealed a causal effect of Blautia on PAC via SBP. These results strengthen the association between the systemic RAAS and GM in humans, and interventions targeting the GM may provide new preventive measures and treatments for hypertension and renal disease.

Gut Microbiota and Microbial Metabolism in Early Risk of Cardiometabolic Disease

Cardiometabolic disease comprises cardiovascular and metabolic dysfunction and underlies the leading causes of morbidity and mortality, both within the United States and worldwide. Commensal microbiota are implicated in the development of cardiometabolic disease. Evidence suggests that the microbiome is relatively variable during infancy and early childhood, becoming more fixed in later childhood and adulthood. Effects of microbiota, both during early development, and in later life, may induce changes in host metabolism that modulate risk mechanisms and predispose toward the development of cardiometabolic disease. In this review, we summarize the factors that influence gut microbiome composition and function during early life and explore how changes in microbiota and microbial metabolism influence host metabolism and cardiometabolic risk throughout life. We highlight limitations in current methodology and approaches and outline state-of-the-art advances, which are improving research and building toward refined diagnosis and treatment options in microbiome-targeted therapies.

Exploring the causal effects of the gut microbiome on serum lipid levels: A two-sample Mendelian randomization analysis

Background

The gut microbiome was reported to be associated with dyslipidemia in previous observational studies. However, whether the composition of the gut microbiome has a causal effect on serum lipid levels remains unclear.

Objective

A two-sample Mendelian randomization (MR) analysis was conducted to investigate the potential causal relationships between gut microbial taxa and serum lipid levels, including low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), total cholesterol (TC), and log-transformed triglyceride (TG) levels.

Materials and methods

Summary statistics of genome-wide association studies (GWASs) for the gut microbiome and four blood lipid traits were obtained from public datasets. Five recognized MR methods were applied to assess the causal estimates, among which, the inverse-variance weighted (IVW) regression was used as the primary MR method. A series of sensitivity analyses were performed to test the robustness of the causal estimates.

Results

The combined results from the five MR methods and sensitivity analysis showed 59 suggestive causal associations and four significant causal associations. In particular, genus Terrisporobacter was associated with higher LDL-C (P _IVW = 3.01 × 10^-6) and TC levels (P _IVW = 2.11 × 10^-4), phylum Actinobacteria was correlated with higher LDL-C level (P _IVW = 4.10 × 10^-4), and genus Oscillospira was associated with lower TG level (P _IVW = 2.19 × 10^-6).

Conclusion

This research may provide novel insights into the causal relationships of the gut microbiome on serum lipid levels and new therapeutic or prevention strategies for dyslipidemia.

Characteristic alterations of gut microbiota in uncontrolled gout

Microbiome research has been on the rise recently for a more in-depth understanding of gout. Meanwhile, there is a need to understand the gut microbiome related to uric acid-lowering drug resistance. In this study, 16S rRNA gene-based microbiota analysis was performed for a total of 65 stool samples from 17 healthy controls and 48 febuxostat-treated gout patients (including 28 controlled subjects with decreased uric acid levels and 20 uncontrolled subjects with non-reduced uric acid levels). Alpha diversity of bacterial community decreased in the healthy control, controlled, and uncontrolled groups. In the case of beta diversity, the bacterial community was significantly different among groups (healthy control, controlled, and uncontrolled groups). Taxonomic biomarker analysis revealed the increased population of g-Bifidobacterium in healthy controls and g-Prevotella in uncontrolled patients. PCR further confirmed this result at the species level. Additionally, functional metagenomics predictions led to the exploration of various functional biomarkers, including purine metabolism. The results of this study can serve as a basis for developing potential new strategies for diagnosing and treating gout from microbiome prospects.