A Fecal Metabolite Signature of Impaired Fasting Glucose: Results From Two Independent Population-Based Cohorts

Microbial Disturbances Caused by Pesticide Exposure and Their Predictive Implications for Gestational Diabetes Mellitus

Previous studies have suggested that pesticide exposure and gut microbiome alterations are associated with gestational diabetes mellitus (GDM) risk. Understanding the complex interactive effect of these factors on GDM is essential. In a cohort of 852 pregnant women, we assessed pesticide levels in serum and analyzed the gut microbiota using 16S rRNA and shotgun metagenomic sequencing. We explored the interactions between pesticides and gut microbiota, assessed their roles in GDM development, and proposed a predictive model based on identified biomarkers. We identified an environmental risk score (ERS), denoting the pesticide mixture level significantly associated with GDM, with the gut microbiota, particularly involving the Dorea branch, playing a crucial mediating role. In addition, we found an interactive effect of pesticide exposure and gut microbiota on GDM risk. Notably, low Prevotella enrichment combined with high ERS arisen from pesticide levels led to a 10.36-fold increased GDM risk. The identified pesticide and gut microbial biomarkers achieved high predictive accuracy for GDM (AUC: 0.833, 95% CI: 0.748-0.918). Collectively, maternal pesticide exposure may induce disrupted microbiome-dependent glycemic alteration, necessitating future assessment of clinical implications. Potential GDM markers can serve as targets for therapeutic intervention caused by pesticides, leading to prevention.

The characteristics of intestinal microbiota in patients with type 2 diabetes and the correlation with the percentage of T-helper cells

Background

Type 2 diabetes (T2D) is related to intestinal microflora changes and immune inflammation. We aimed to investigate the pattern of intestinal flora-systematic T helper (Th) cell linkage in T2D patients.

Methods

Participants with T2D diagnosed by physicians and healthy controls were enrolled in the study. The Th1, Th2, and Th17 cells from the peripheral blood were assessed by flow cytometry. The feces were collected. The V3-V4 variable region of 16S rRNA was sequenced and analyzed using bioinformatics. Principal coordinate analysis (PCoA) and non-metric multidimensional scaling (NMDS) analysis were performed to assess the beta diversity. The linear discriminant analysis (LDA) effect size (LEfSe) method was applied to identify amicrobial taxon specific to T2D. The Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) was conducted to identify the metabolic pathways. A network analysis was conducted by constructing a co-occurrence network.

Results

The percentages of the Th1 and Th17 cells in the peripheral blood were higher in patients with T2D than in controls. Among the top 30 genera of the intestinal microbiota, the levels of Lachnospiraceae_NK4A136_group, Ruminococcaceae_UCG002, and Eubacterium_hallii_group were lower in the patients with T2D than in controls. In the LEfSe analysis, it was observed that the Lachnospiraceae and Ruminococcaceae families were significantly different between patients with T2D and controls. Moreover, the Th1/Th2 ratio was positively correlated with the abundance of the Lachnoclostridium and Ruminococcus_torques_group genera. In the network analysis, the Th1/Th2 ratio, Ruminococcaceae_UCG-002, and Lachnospiraceae_NK4A136_group were the important nodes.

Conclusion

This study provided a preliminary picture of the crosstalk between the intestinal microbiome and systematic Th cells in patients with T2D. The findings of the study suggested that the network relationship among the intestinal microbiota, metabolites, and CD4+T lymphocyte immunity was unbalanced in the patients with T2D, which might have promoted the development of T2D. This presents a therapeutic opportunity to modulate gut immune reaction and then chronic inflammation by manipulating microbiome-specific Th-cell response.

Exploring the design of clinical research studies on the efficacy mechanisms in type 2 diabetes mellitus

This review examines the complexities of Type 2 Diabetes Mellitus (T2DM), focusing on the critical role of integrating omics technologies with traditional experimental methods. It underscores the advancements in understanding the genetic diversity of T2DM and emphasizes the evolution towards personalized treatment modalities. The paper analyzes a variety of omics approaches, including genomics, methylation, transcriptomics, proteomics, metabolomics, and intestinal microbiomics, delineating their substantial contributions to deciphering the multifaceted mechanisms underlying T2DM. Furthermore, the review highlights the indispensable role of non-omics experimental techniques in comprehending and managing T2DM, advocating for their integration in the development of tailored medicine and precision treatment strategies. By identifying existing research gaps and suggesting future research trajectories, the review underscores the necessity for a comprehensive, multidisciplinary approach. This approach synergistically combines clinical insights with cutting-edge biotechnologies, aiming to refine the management and therapeutic interventions of T2DM, and ultimately enhancing patient outcomes. This synthesis of knowledge and methodologies paves the way for innovative advancements in T2DM research, fostering a deeper understanding and more effective treatment of this complex condition.

Vitamin A carotenoids, but not retinoids, mediate the impact of a healthy diet on gut microbial diversity

BACKGROUND

Vitamin A is essential for physiological processes like vision and immunity. Vitamin A's effect on gut microbiome composition, which affects absorption and metabolism of other vitamins, is still unknown. Here we examined the relationship between gut metagenome composition and six vitamin A-related metabolites (two retinoid: -retinol, 4 oxoretinoic acid (oxoRA) and four carotenoid metabolites, including beta-cryptoxanthin and three carotene diols).

METHODS

We included 1053 individuals from the TwinsUK cohort with vitamin A-related metabolites measured in serum and faeces, diet history, and gut microbiome composition assessed by shotgun metagenome sequencing. Results were replicated in 327 women from the ZOE PREDICT-1 study.

RESULTS

Five vitamin A-related serum metabolites were positively correlated with microbiome alpha diversity (r = 0.15 to r = 0.20, p < 4 × 10-6). Carotenoid compounds were positively correlated with the short-chain fatty-acid-producing bacteria Faecalibacterium prausnitzii and Coprococcus eutactus. Retinol was not associated with any microbial species. We found that gut microbiome composition could predict circulating levels of carotenoids and oxoretinoic acid with AUCs ranging from 0.66 to 0.74 using random forest models, but not retinol (AUC = 0.52). The healthy eating index (HEI) was strongly associated with gut microbiome diversity and with all carotenoid compounds, but not retinoids. We investigated the mediating role of carotenoid compounds on the effect of a healthy diet (HEI) on gut microbiome diversity, finding that carotenoids significantly mediated between 18 and 25% of the effect of HEI on gut microbiome alpha diversity.

CONCLUSIONS

Our results show strong links between circulating carotene compounds and gut microbiome composition and potential links to a healthy diet pattern.

Machine Learning Metabolomics Profiling of Dietary Interventions from a Six-Week Randomised Trial

Metabolomics can uncover physiological responses to prebiotic fibre and omega-3 fatty acid supplements with known health benefits and identify response-specific metabolites. We profiled 534 stool and 799 serum metabolites in 64 healthy adults following a 6-week randomised trial comparing daily omega-3 versus inulin supplementation. Elastic net regressions were used to separately identify the serum and stool metabolites whose change in concentration discriminated between the two types of supplementations. Random forest was used to explore the gut microbiome's contribution to the levels of the identified metabolites from matching stool samples. Changes in serum 3-carboxy-4-methyl-5-propyl-2-furanpropanoate and indoleproprionate levels accurately discriminated between fibre and omega-3 (area under the curve (AUC) = 0.87 [95% confidence interval (CI): 0.63-0.99]), while stool eicosapentaenoate indicated omega-3 supplementation (AUC = 0.86 [95% CI: 0.64-0.98]). Univariate analysis also showed significant increases in indoleproprionate with fibre, 3-carboxy-4-methyl-5-propyl-2-furanpropanoate, and eicosapentaenoate with omega-3. Out of these, only the change in indoleproprionate was partly explained by changes in the gut microbiome composition (AUC = 0.61 [95% CI: 0.58-0.64] and Rho = 0.21 [95% CI: 0.08-0.34]) and positively correlated with the increase in the abundance of the genus Coprococcus (p = 0.005). Changes in three metabolites discriminated between fibre and omega-3 supplementation. The increase in indoleproprionate with fibre was partly explained by shifts in the gut microbiome, particularly Coprococcus, previously linked to better health.

Uridine and its role in metabolic diseases, tumors, and neurodegenerative diseases

Uridine is a pyrimidine nucleoside found in plasma and cerebrospinal fluid with a concentration higher than the other nucleosides. As a simple metabolite, uridine plays a pivotal role in various biological processes. In addition to nucleic acid synthesis, uridine is critical to glycogen synthesis through the formation of uridine diphosphate glucose in which promotes the production of UDP-GlcNAc in the hexosamine biosynthetic pathway and supplies UDP-GlcNAc for O-GlcNAcylation. This process can regulate protein modification and affect its function. Moreover, Uridine has an effect on body temperature and circadian rhythms, which can regulate the metabolic rate and the expression of metabolic genes. Abnormal levels of blood uridine have been found in people with diabetes and obesity, suggesting a link of uridine dysregulation and metabolic disorders. At present, the role of uridine in glucose metabolism and lipid metabolism is controversial, and the mechanism is not clear, but it shows the trend of long-term damage and short-term benefit. Therefore, maintaining uridine homeostasis is essential for maintaining basic functions and normal metabolism. This article summarizes the latest findings about the metabolic effects of uridine and the potential of uridine metabolism as therapeutic target in treatment of metabolic disorders.