Mechanisms of gut microbiota-immune-host interaction on glucose regulation in type 2 diabetes

A 12-Week, Single-Centre, Randomised, Double-Blind, Placebo-Controlled, Parallel-Design Clinical Trial for the Evaluation of the Efficacy and Safety of Lactiplantibacillus plantarum SKO-001 in Reducing Body Fat

There is growing evidence linking gut microbiota to overall health, including obesity risk and associated diseases. Lactiplantibacillus plantarum SKO-001, a probiotic strain isolated from Angelica gigas, has been reported to reduce obesity by controlling the gut microbiome. In this double-blind, randomised clinical trial, we aimed to evaluate the efficacy and safety of SKO-001 in reducing body fat. We included 100 participants randomised into SKO-001 or placebo groups (1:1) for 12 weeks. Dual-energy X-ray absorptiometry was used to objectively evaluate body fat reduction. Body fat percentage (p = 0.016), body fat mass (p = 0.02), low-density lipoprotein-cholesterol levels (p = 0.025), and adiponectin levels (p = 0.023) were lower in the SKO-001 group than in the placebo group after 12 weeks of SKO-001 consumption. In the SKO-001 group, the subcutaneous fat area (p = 0.003), total cholesterol levels (p = 0.003), and leptin levels (p = 0.014) significantly decreased after 12 weeks of SKO-001 consumption compared with baseline values. Additionally, SKO-001 did not cause any severe adverse reactions. In conclusion, SKO-001 is safe and effective for reducing body fat and has the potential for further clinical testing in humans.

The mediating role of dietary inflammatory index on the association between eating breakfast and depression: Based on NHANES 2007-2018

BACKGROUND

Depression is a significant, pervasive, global public health problem, associated with many factors, such as diet, social factors, and lifestyle habits. We aimed to evaluate the association between eating breakfast, dietary inflammatory index (DII) and depression, and to verify the mediating role of DII on the effect of eating breakfast on depression.

METHODS

21,865 participants from the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2018 were included in this study. Binary logistic regression and mediated effect analysis were conducted to analyze the associations between eating breakfast, DII and depression. Dietary inflammation was divided into pro-inflammatory diet and anti-inflammatory diet according to the DII.

RESULTS

Both pro-inflammatory diet and skipping breakfast were risk factors for depression. After adjusting for covariables, compared with participants reporting breakfast in both recalls, reporting breakfast in one recall had a higher OR 95%CI (1.54(1.20, 1.98)) of depression. These associations in stratified analysis and sensitivity analysis without cardiovascular diseases (CVD) and diabetes were robust. DII mediated the association between eating breakfast and depression, the proportion of participants who reported breakfast in one recall and no recall was 26.15 % and 26.67 %, respectively.

LIMITATIONS

This was a cross-sectional study that couldn't argue for the cause-effect relationship. Moreover, the confounding factor regarding medication use was not accounted for due to limited data.

CONCLUSIONS

Skipping breakfast may increase the risk of depression by raising DII. And our study supported the essential role of regular breakfast and the anti-inflammatory diet in reducing the risk of depression.

Comparative study of type 2 diabetes mellitus-associated gut microbiota between the Dai and Han populations

BACKGROUND

The global prevalence of type 2 diabetes mellitus (T2DM) is increasing. T2DM is associated with alterations of the gut microbiota, which can be affected by age, illness, and genetics. Previous studies revealed that there are discriminating microbiota compositions between the Dai and the Han populations. However, the specific gut microbiota differences between the two populations have not been elucidated.

AIM

To compare the gut microbiota differences in subjects with and without T2DM in the Dai and Han populations.

METHODS

A total of 35 subjects of the Han population (including 15 healthy children, 8 adult healthy controls, and 12 adult T2DM patients) and 32 subjects of the Dai population (including 10 healthy children, 10 adult healthy controls, and 12 adult T2DM patients) were enrolled in this study. Fasting venous blood samples were collected from all the subjects for biochemical analysis. Fecal samples were collected from all the subjects for DNA extraction and 16S rRNA sequencing, which was followed by analyses of the gut microbiota composition.

RESULTS

No significant difference in alpha diversity was observed between healthy children and adults. The diversity of gut microbiota was decreased in T2DM patients compared to the healthy adults in both the Dai and Han populations. There was a significant difference in gut microbiota between healthy children and healthy adults in the Han population with an increased abundance of Bacteroidetes and decreased Firmicutes in children. However, this difference was less in the Dai population. Significant increases in Bacteroidetes in the Han population and Proteobacteria in the Dai population and decreases in Firmicutes in both the Han and Dai population were observed in T2DM patients compared to healthy adults. Linear discriminant analysis Effect Size analysis also showed that the gut microbiota was different between the Han and Dai populations in heathy children, adults, and T2DM patients. Four bacteria were consistently increased and two consistently decreased in the Han population compared to the Dai population.

CONCLUSION

Differences in gut microbiota were found between the Han and Dai populations. A significant increase in Bacteroidetes was related to the occurrence of T2DM in the Han population, while a significant increase in Proteobacteria was related to the occurrence of T2DM in the Dai population.

Predicting potential microbe-disease associations based on auto-encoder and graph convolution network

The increasing body of research has consistently demonstrated the intricate correlation between the human microbiome and human well-being. Microbes can impact the efficacy and toxicity of drugs through various pathways, as well as influence the occurrence and metastasis of tumors. In clinical practice, it is crucial to elucidate the association between microbes and diseases. Although traditional biological experiments accurately identify this association, they are time-consuming, expensive, and susceptible to experimental conditions. Consequently, conducting extensive biological experiments to screen potential microbe-disease associations becomes challenging. The computational methods can solve the above problems well, but the previous computational methods still have the problems of low utilization of node features and the prediction accuracy needs to be improved. To address this issue, we propose the DAEGCNDF model predicting potential associations between microbes and diseases. Our model calculates four similar features for each microbe and disease. These features are fused to obtain a comprehensive feature matrix representing microbes and diseases. Our model first uses the graph convolutional network module to extract low-rank features with graph information of microbes and diseases, and then uses a deep sparse Auto-Encoder to extract high-rank features of microbe-disease pairs, after which the low-rank and high-rank features are spliced to improve the utilization of node features. Finally, Deep Forest was used for microbe-disease potential relationship prediction. The experimental results show that combining low-rank and high-rank features helps to improve the model performance and Deep Forest has better classification performance than the baseline model.

Bi-Directional Interactions between Glucose-Lowering Medications and Gut Microbiome in Patients with Type 2 Diabetes Mellitus: A Systematic Review

Background: Although common drugs for treating type 2 diabetes (T2D) are widely used, their therapeutic effects vary greatly. The interaction between the gut microbiome and glucose-lowering drugs is one of the main contributors to the variability in T2D progression and response to therapy. On the one hand, glucose-lowering drugs can alter gut microbiome components. On the other hand, specific gut microbiota can influence glycemic control as the therapeutic effects of these drugs. Therefore, this systematic review assesses the bi-directional relationships between common glucose-lowering drugs and gut microbiome profiles. Methods: A systematic search of Embase, Web of Science, PubMed, and Google Scholar databases was performed. Observational studies and randomised controlled trials (RCTs), published from inception to July 2023, comprising T2D patients and investigating bi-directional interactions between glucose-lowering drugs and gut microbiome, were included. Results: Summarised findings indicated that glucose-lowering drugs could increase metabolic-healthy promoting taxa (e.g., Bifidobacterium) and decrease harmful taxa (e.g., Bacteroides and Intestinibacter). Our findings also showed a significantly different abundance of gut microbiome taxa (e.g., Enterococcus faecium (i.e., E. faecium)) in T2D patients with poor compared to optimal glycemic control. Conclusions: This review provides evidence for glucose-lowering drug and gut microbiome interactions, highlighting the potential of gut microbiome modulators as co-adjuvants for T2D treatment.