Effects of vancomycin-induced gut microbiome alteration on the pharmacodynamics of metformin in healthy male subjects

Metformin use associated with lower rate of hospitalization for influenza in individuals with diabetes

AIM

To investigate if patients with diabetes taking metformin have better outcomes versus those not taking metformin following an emergency room visit for influenza.

METHODS

Using electronic medical records, we performed a retrospective chart review of all adult patients with a diagnosis of diabetes seen in any Duke University Medical Center-affiliated emergency department for influenza over a 6-year period. We documented patient characteristics and comorbidities, and compared outcomes for patients taking metformin versus patients not taking metformin using both univariable and multivariable analyses. Our primary outcome was hospital admission rate. Secondary outcomes were in-hospital length of stay and in-hospital death.

RESULTS

Our cohort included 1023 adult patients with diabetes, of whom 59.9% were female. The mean age was 62.9 years, 58.4% were African American, 36.1% were White, and 81.9% were obese or overweight. Of these patients, 347 (34%) were taking metformin. Patients with diabetes taking metformin were less likely to be hospitalized following an emergency department visit for influenza than patients with diabetes not taking metformin (56.8% vs. 70.1%; p < 0.001). Of those patients admitted, there was no statistically significant difference in length of stay or death.

CONCLUSIONS

In patients with diabetes, metformin use is associated with lower rate of hospitalization following an emergency department visit for influenza.

Human gut microbiome: Therapeutic opportunities for metabolic syndrome-Hype or hope?

Shifts in gut microbiome composition and metabolic disorders are associated with one another. Clinical studies and experimental data suggest a causal relationship, making the gut microbiome an attractive therapeutic goal. Diet, intake of probiotics or prebiotics and faecal microbiome transplantation (FMT) are methods to alter a person's microbiome composition. Although FMT may allow establishing a proof of concept to use microbiome modulation to treat metabolic disorders, studies show mixed results regarding the effects on metabolic parameters as well as on the composition of the microbiome. This review summarizes the current knowledge on diet, probiotics, prebiotics and FMT to treat metabolic diseases, focusing on studies that also report alterations in microbiome composition. Furthermore, clinical trial results on the effects of common drugs used to treat metabolic diseases are synopsized to highlight the bidirectional relationship between the microbiome and metabolic diseases. In conclusion, there is clear evidence that microbiome modulation has the potential to influence metabolic diseases; however, it is not possible to distinguish which intervention is the most successful. In addition, a clear commitment from all stakeholders is necessary to move forward in the direction of developing targeted interventions for microbiome modulation.

Comparison of fidaxomicin, thuricin CD, vancomycin and nisin highlights the narrow spectrum nature of thuricin CD

Vancomycin and metronidazole are commonly used treatments for Clostridioides difficile infection (CDI). However, these antibiotics have been associated with high levels of relapse in patients. Fidaxomicin is a new treatment for CDI that is described as a narrow spectrum antibiotic that is minimally active on the commensal bacteria of the gut microbiome. The aim of this study was to compare the effect of fidaxomicin on the human gut microbiome with a number of narrow (thuricin CD) and broad spectrum (vancomycin and nisin) antimicrobials. The spectrum of activity of each antimicrobial was tested against 47 bacterial strains by well-diffusion assay. Minimum inhibitory concentrations (MICs) were calculated against a select number of these strains. Further, a pooled fecal slurry of 6 donors was prepared and incubated for 24 h with 100 µM of each antimicrobial in a mini-fermentation system together with a no-treatment control. Fidaxomicin, vancomycin, and nisin were active against most gram positive bacteria tested in vitro, although fidaxomicin and vancomycin produced larger zones of inhibition compared to nisin. In contrast, the antimicrobial activity of thuricin CD was specific to C. difficile and some Bacillus spp. The MICs showed similar results. Thuricin CD exhibited low MICs (<3.1 µg/mL) for C. difficile and Bacillus firmus, whereas fidaxomicin, vancomycin, and nisin demonstrated lower MICs for all other strains tested when compared to thuricin CD. The narrow spectrum of thuricin CD was also observed in the gut model system. We conclude that the spectrum of activity of fidaxomicin is comparable to that of the broad-spectrum antibiotic vancomycin in vitro and the broad spectrum bacteriocin nisin in a complex community.

Managing the clinical effects of drug-induced intestinal dysbiosis with a focus to antibiotics: Challenges and opportunities

The term "intestinal dysbiosis" is used for indicating change(s) of the intestinal microbiota which have been associated with the development of diseases and the deterioration of disease treatments in humans. In this review, documented clinical effects of drug-induced intestinal dysbiosis are briefly presented, and methodologies which could be considered for the management of drug-induced intestinal dysbiosis based on clinical data are critically reviewed. Until relevant methodologies are optimized and/or their effectiveness to the general population is confirmed, and, since drug-induced intestinal dysbiosis refers predominantly to antibiotic-specific intestinal dysbiosis, a pharmacokinetically-based approach for mitigating the impact of antimicrobial therapy on intestinal dysbiosis is proposed.

Gut microbiome health and dysbiosis: A clinical primer

The gut microbiome has been referred to as the "forgotten organ." Although much about the gut microbiome remains incompletely understood, data on its clinical importance is emerging at rapid speed. Many practicing clinicians may be unaware of the essential role that the microbiome plays in both health and disease. This review aims to improve clinical understanding of the gut microbiome by discussing key terminology and foundational concepts. The role of a healthy microbiome in normal host function is described, as well as the consequences of a disrupted microbiome (i.e., dysbiosis). Management strategies to restore the gut microbiome from a disrupted to a healthy state are also briefly discussed. Lastly, we review emerging areas for therapeutic potential and opportunity to bring determinants of microbiome health from the bench to bedside.

Metformin reverse minocycline to inhibit minocycline-resistant Acinetobacter baumannii by destroy the outer membrane and enhance membrane potential in vitro

Background

Acinetobacter baumannii (A. baumannii) is an opportunistic pathogen and has emerged as one of the most troublesome pathogens. Drug resistance in A. baumannii has been reported on a global scale. Minocycline was found to be active against multi-drug resistant A. baumannii and was approved by the FDA for the infections caused by sensitive strains of A. baumannii. However, the emergence of minocycline resistance and its toxic effects still need to be addressed. Therefore, this study aimed to evaluate the synergistic effects of metformin combined with minocycline on minocycline-resistant A. baumannii.

Results

The effect of metformin on the antibacterial activity of minocycline was determined by checkerboard and time-killing assay. Further, it was observed by biofilm formation assay that metformin combination with minocycline can inhibit the formation of biofilm. Outer membrane integrity, membrane permeability, membrane potential and reactive oxygen species (ROS) were monitored to explore the underlying synergistic mechanisms of metformin on minocycline. And the results shown that metformin can destroy the outer membrane of A. baumannii, enhance its membrane potential, but does not affect the membrane permeability and ROS.

Conclusion

These findings suggested that the combination of metformin and minocycline has the potential for rejuvenating the activity of minocycline against minocycline-resistant A. baumannii.

Interaction between Plasma Metabolomics and Intestinal Microbiome in db/db Mouse, an Animal Model for Study of Type 2 Diabetes and Diabetic Kidney Disease

Evidence has demonstrated that either metabolites or intestinal microbiota are involved in the pathogenesis of type 2 diabetes (T2D) and diabetic kidney disease (DKD). To explore the interaction between plasma metabolomics and intestinal microbiome in the progress of T2D-DKD, in the current study, we analyzed metabolomics in the plasma of db/db mice with liquid chromatography–mass spectrometry and also examined intestinal prokaryotes and entire gut microbiome dysbiosis at the genus level with both 16S rDNA and metagenomic sequencing techniques. We found that Negativibacillus and Rikenella were upregulated, while Akkermansia, Candidatus, Erysipelatoclostridium and Ileibacterium were downregulated in the colon of db/db mice compared with non-diabetic controls. In parallel, a total of 91 metabolites were upregulated, while 23 were downregulated in the plasma of db/db mice. The top five upregulated metabolites included D-arabinose 5-phosphate, estrone 3-sulfate, L-theanine, 3′-aenylic acid and adenosine 5′-monophosphate, and the five most significantly downregulated metabolites were aurohyocholic acid sodium salt, calcium phosphorylcholine chloride, tauro-alpha-muricholic acid sodium salt, galactinol and phosphocholine. These plasma metabolites were interacted with intestinal microbiomes, which are mainly involved in the pathways related to the biosynthesis of unsaturated fatty acids, fatty acid elongation, steroid biosynthesis, and D-arginine and D-ornithine metabolism. In the differential metabolites, N-acetyl-L-ornithine, ornithine and L-kyn could be metabolized by the correspondingly differential ontology genes in the intestinal metagenome. The current study thereby provides evidence for a gut–metabolism–kidney axis in the metabolism of db/db mice, in which the gut microbiome and circulating metabolomics interact, and suggests that information from this axis may contribute to our understanding of T2D and DKD pathogenesis.

Association of gut microbiota with glycaemic traits and incident type 2 diabetes, and modulation by habitual diet: a population-based longitudinal cohort study in Chinese adults

AIMS/HYPOTHESIS

The gut microbiome is mainly shaped by diet, and varies across geographical regions. Little is known about the longitudinal association of gut microbiota with glycaemic control. We aimed to identify gut microbiota prospectively associated with glycaemic traits and type 2 diabetes in a geographically diverse population, and examined the cross-sectional association of dietary or lifestyle factors with the identified gut microbiota.

METHODS

The China Health and Nutrition Survey is a population-based longitudinal cohort covering 15 provinces/megacities across China. Of the participants in that study, 2772 diabetes-free participants with a gut microbiota profile based on 16S rRNA analysis were included in the present study (age 50.8 ± 12.7 years, mean ± SD). Using a multivariable-adjusted linear mixed-effects model, we examined the prospective association of gut microbiota with glycaemic traits (fasting glucose, fasting insulin, HbA_1c and HOMA-IR). We constructed a healthy microbiome index (HMI), and used Poisson regression to examine the relationship between the HMI and incident type 2 diabetes. We evaluated the association of dietary or lifestyle factors with the glycaemic trait-related gut microbiota using a multivariable-adjusted linear regression model.

RESULTS

After follow-up for 3 years, 123 incident type 2 diabetes cases were identified. We identified 25 gut microbial genera positively or inversely associated with glycaemic traits. The newly created HMI (per SD unit) was inversely associated with incident type 2 diabetes (risk ratio 0.69, 95% CI 0.58, 0.84). Furthermore, we found that several microbial genera that were favourable for the glycaemic trait were consistently associated with healthy dietary habits (higher consumption of vegetable, fruit, fish and nuts).

CONCLUSIONS/INTERPRETATION

Our results revealed multiple gut microbiota prospectively associated with glycaemic traits and type 2 diabetes in a geographically diverse population, and highlighted the potential of gut microbiota-based diagnosis or therapy for type 2 diabetes.

DATA AVAILABILITY

The code for data analysis associated with the current study is available at https://github.com/wenutrition/Microbiota-T2D-CHNS.

Anti-Hyperglycemic Agents in the Adjuvant Treatment of Sepsis: Improving Intestinal Barrier Function

Intestinal barrier injury and hyperglycemia are common in patients with sepsis. Bacteria translocation and systemic inflammatory response caused by intestinal barrier injury play a significant role in sepsis occurrence and deterioration, while hyperglycemia is linked to adverse outcomes in sepsis. Previous studies have shown that hyperglycemia is an independent risk factor for intestinal barrier injury. Concurrently, increasing evidence has indicated that some anti-hyperglycemic agents not only improve intestinal barrier function but are also beneficial in managing sepsis-induced organ dysfunction. Therefore, we assume that these agents can block or reduce the severity of sepsis by improving intestinal barrier function. Accordingly, we explicated the connection between sepsis, intestinal barrier, and hyperglycemia, overviewed the evidence on improving intestinal barrier function and alleviating sepsis-induced organ dysfunction by anti-hyperglycemic agents (eg, metformin, peroxisome proliferators activated receptor-γ agonists, berberine, and curcumin), and summarized some common characteristics of these agents to provide a new perspective in the adjuvant treatment of sepsis.

A validated simple LC-MS/MS method for quantifying trimethylamine N-oxide (TMAO) using a surrogate matrix and its clinical application

Trimethylamine N-oxide (TMAO) is a small molecular amine oxide generated from dietary choline and carnitine through intestinal microbial metabolism. Recently, TMAO has attracted much public attention as its role in disease progression has been proven in many clinical studies. The plasma concentration of TMAO in humans was found to be positively associated with the increased risk of many diseases including cardiovascular diseases and chronic kidney diseases. To achieve accurate and sensitive quantitation of TMAO for clinical applications, we established and validated a simple quantitative method using a liquid chromatography tandem mass spectrometry (LC-MS/MS) system. We constructed an eight-point calibration curve in an artificial surrogate matrix instead of the commonly used biological matrices to avoid interference from the endogenous TMAO. The calibration curve showed excellent linearity in the range of 1 to 5,000 ng/mL, with a correlation coefficient (R²) higher than 0.996 in each validation batch. Moreover, both the intra-day and inter-day assays achieved satisfactory precision and accuracy results ranging from 1.65–7.15% and 96.36–111.43%, respectively. Further, this method was cross-validated using a human plasma matrix and applied to a clinical pharmacology study. Overall, these results demonstrate that the developed quantitation method is applicable in clinical research for monitoring disease progression and evaluating drug effects.

Changes in the gut microbiome influence the hypoglycemic effect of metformin through the altered metabolism of branched-chain and nonessential amino acids

AIMS

Although metformin has been reported to affect the gut microbiome, the mechanism has not been fully determined. We explained the potential underlying mechanisms of metformin through a multiomics approach.

METHODS

An open-label and single-arm clinical trial involving 20 healthy Korean was conducted. Serum glucose and insulin concentrations were measured, and stool samples were collected to analyze the microbiome. Untargeted metabolomic profiling of plasma, urine, and stool samples was performed by GC-TOF-MS. Network analysis was applied to infer the mechanism of the hypoglycemic effect of metformin.

RESULTS

The relative abundances of Escherichia, Romboutsia, Intestinibacter, and Clostridium were changed by metformin treatment. Additionally, the relative abundances of metabolites, including carbohydrates, amino acids, and fatty acids, were changed. These changes were correlated with energy metabolism, gluconeogenesis, and branched-chain amino acid metabolism, which are major metabolic pathways related to the hypoglycemic effect.

CONCLUSIONS

We observed that specific changes in metabolites may affect hypoglycemic effects through both pathways related to AMPK activation and microbial changes. Energy metabolism was mainly related to hypoglycemic effects. In particular, branched-chain amino acid metabolism and gluconeogenesis were related to microbial metabolites. Our results will help uncover the potential underlying mechanisms of metformin through AMPK and the microbiome.