Gut microbiota differs in composition between adults with type 1 diabetes with or without depression and healthy control participants: a case-control study

Microbiome composition and central serotonergic activity in patients with depression and type 1 diabetes

The role of gut-brain axis functioning gains growing attention in research on the pathophysiology of major depressive disorders. Here, especially consequences of altered microbiota composition on tryptophan metabolism resulting in altered serotonergic neurotransmission in the central nervous system (CNS) have reached a central position. Previous research, however, mainly focused on either microbiota and peripheral serotonin levels or central serotonergic neurotransmission. The present study aimed to combine the analysis of microbiota composition and central serotonergic activity using a valid neurophysiological indicator. We recruited 19 adult patients with type 1 diabetes and depression (D + D; 7 males), 19 patients with type 1 diabetes (D-; 7 male), and 20 healthy participants (HC; 7 males). Next to the analysis of fecal microbiota regarding α- and β-diversity, the loudness dependence of auditory evoked potential (LDAEP) was investigated, a non-invasive measurement of central serotonergic activity. High α-diversity was associated with high LDAEP, i.e., low serotonergic activity, in patients with diabetes and additional depression. Furthermore, relative abundances of bacterial families belonging to Bacteroidetes, Proteobacteria and Firmicutes were shown to have an impact on central serotonergic activity. This finding was supported by a tendency indicating an association of central serotonergic activity with the Bacteroidetes-Firmicutes ratio in both patients' groups. Together, this data suggests that the guts' microbiota composition might play an important role in regulating the central serotonergic activity in the brain.

Predicting the role of the human gut microbiome in type 1 diabetes using machine-learning methods

Gut microbes is a crucial factor in the pathogenesis of type 1 diabetes (T1D). However, it is still unclear which gut microbiota are the key factors affecting T1D and their influence on the development and progression of the disease. To fill these knowledge gaps, we constructed a model to find biomarker from gut microbiota in patients with T1D. We first identified microbial markers using Linear discriminant analysis Effect Size (LEfSe) and random forest (RF) methods. Furthermore, by constructing co-occurrence networks for gut microbes in T1D, we aimed to reveal all gut microbial interactions as well as major beneficial and pathogenic bacteria in healthy populations and type 1 diabetic patients. Finally, PICRUST2 was used to predict Kyoto Encyclopedia of Genes and Genomes (KEGG) functional pathways and KO gene levels of microbial markers to investigate the biological role. Our study revealed that 21 identified microbial genera are important biomarker for T1D. Their AUC values are 0.962 and 0.745 on discovery set and validation set. Functional analysis showed that 10 microbial genera were significantly positively associated with D-arginine and D-ornithine metabolism, spliceosome in transcription, steroid hormone biosynthesis and glycosaminoglycan degradation. These genera were significantly negatively correlated with steroid biosynthesis, cyanoamino acid metabolism and drug metabolism. The other 11 genera displayed an inverse correlation. In summary, our research identified a comprehensive set of T1D gut biomarkers with universal applicability and have revealed the biological consequences of alterations in gut microbiota and their interplay. These findings offer significant prospects for individualized management and treatment of T1D.

Gut microbiota induced abnormal amino acids and their correlation with diabetic retinopathy

AIM

To explore the correlation of gut microbiota and the metabolites with the progression of diabetic retinopathy (DR) and provide a novel strategy to elucidate the pathological mechanism of DR.

METHODS

The fecal samples from 32 type 2 diabetes patients with proliferative retinopathy (PDR), 23 with non-proliferative retinopathy (NPDR), 27 without retinopathy (DM), and 29 from the sex-, age- and BMI- matched healthy controls (29 HC) were analyzed by 16S rDNA gene sequencing. Sixty fecal samples from PDR, DM, and HC groups were assayed by untargeted metabolomics. Fecal metabolites were measured using liquid chromatography-mass spectrometry (LC-MS) analysis. Associations between gut microbiota and fecal metabolites were analyzed.

RESULTS

A cluster of 2 microbiome and 12 metabolites accompanied with the severity of DR, and the close correlation of the disease progression with PDR-related microbiome and metabolites were found. To be specific, the structure of gut microbiota differed in four groups. Diversity and richness of gut microbiota were significantly lower in PDR and NPDR groups, than those in DM and HC groups. A cluster of microbiome enriched in PDR group, including Pseudomonas, Ruminococcaceae-UCG-002, Ruminococcaceae-UCG-005, Christensenellaceae-R-7, was observed. Functional analysis showed that the glucose and nicotinate degradations were significantly higher in PDR group than those in HC group. Arginine, serine, ornithine, and arachidonic acid were significantly enriched in PDR group, while proline was enriched in HC group. Functional analysis illustrated that arginine biosynthesis, lysine degradation, histidine catabolism, central carbon catabolism in cancer, D-arginine and D-ornithine catabolism were elevated in PDR group. Correlation analysis revealed that Ruminococcaceae-UCG-002 and Christensenellaceae-R-7 were positively associated with L-arginine, ornithine levels in fecal samples.

CONCLUSION

This study elaborates the different microbiota structure in the gut from four groups. The relative abundance of Ruminococcaceae-UCG-002 and Parabacteroides are associated with the severity of DR. Amino acid and fatty acid catabolism is especially disordered in PDR group. This may help provide a novel diagnostic parameter for DR, especially PDR.

Microbial and metabolomic profiles of type 1 diabetes with depression: A case-control study

BACKGROUND

Depression is the most common psychological disorder in patients with type 1 diabetes (T1D). However, the characteristics of microbiota and metabolites in these patients remain unclear. This study aimed to investigate microbial and metabolomic profiles and identify novel biomarkers for T1D with depression.

METHODS

A case-control study was conducted in a total of 37 T1D patients with depression (TD+), 35 T1D patients without depression (TD-), and 29 healthy controls (HCs). 16S rRNA gene sequencing and liquid chromatography-mass spectrometry (LC-MS) metabolomics analysis were conducted to investigate the characteristics of microbiota and metabolites. The association between altered microbiota and metabolites was explored by Spearman's rank correlation and visualized by a heatmap. The microbial signatures to discriminate TD+ from TD- were identified by a random forest (RF) classifying model.

RESULTS

In microbiota, 15 genera enriched in TD- and 2 genera enriched in TD+, and in metabolites, 14 differential metabolites (11 upregulated and 3 downregulated) in TD+ versus TD- were identified. Additionally, 5 genera (including Phascolarctobacterium, Butyricimonas, and Alistipes from altered microbiota) demonstrated good diagnostic power (area under the curve [AUC] = 0.73; 95% CI, 0.58-0.87). In the correlation analysis, Butyricimonas was negatively correlated with glutaric acid (r = -0.28, p = 0.015) and malondialdehyde (r = -0.30, p = 0.012). Both Phascolarctobacterium (r = 0.27, p = 0.022) and Alistipes (r = 0.31, p = 0.009) were positively correlated with allopregnanolone.

CONCLUSIONS

T1D patients with depression were characterized by unique profiles of gut microbiota and serum metabolites. Phascolarctobacterium, Butyricimonas, and Alistipes could predict the risk of T1D with depression. These findings provide further evidence that the microbiota-gut-brain axis is involved in T1D with depression.

Long-Term Impact of Childhood Adversity on the Gut Microbiome of Nursing Students

Adverse childhood experiences (ACEs) encompass negative, stressful, and potentially traumatic events during childhood, impacting physical and mental health outcomes in adulthood. Limited studies suggest ACEs can have short-term effects on children's gut microbiomes and adult cognitive performance under stress. Nevertheless, the long-term effects of ACEs experienced during adulthood remain unexplored. Thus, this study aimed to assess the long-term effects of ACEs on the gut microbiota of adult nursing students. We employed a multidimensional approach, combining 16S rRNA sequencing, bioinformatics tools, and machine learning to predict functional capabilities. High-ACE individuals had an increased abundance of Butyricimonas spp. and Prevotella spp. and decreased levels of Clostridiales, and Lachnospira spp. Prevotella abundance correlated negatively with L-glutamate and L-glutamine biosynthesis, potentially impacting intestinal tissue integrity. While nursing students with high ACE reported increased depression, evidence for a direct gut microbiota-depression relationship was inconclusive. High-ACE individuals also experienced a higher prevalence of diarrhea. These findings highlight the long-lasting impact of ACEs on the gut microbiota and its functions in adulthood, particularly among nursing students. Further research is warranted to develop targeted interventions and strategies for healthcare professionals, optimizing overall health outcomes.

Gut microbiome and blood glucose control in type 1 diabetes: a systematic review

Objective

The risk of developing micro- and macrovascular complications is higher for individuals with type 1 diabetes (T1D). Numerous studies have indicated variations in gut microbial composition between healthy individuals and those with T1D. These changes in the gut ecosystem may lead to inflammation, modifications in intestinal permeability, and alterations in metabolites. Such effects can collectively impact the metabolic regulation system, thereby influencing blood glucose control. This review aims to explore the relationship between the gut microbiome, inflammation, and blood glucose parameters in patients with T1D.

Methods

Google Scholar, PubMed, and Web of Science were systematically searched from 2003 to 2023 using the following keywords: "gut microbiota," "gut microbiome," "bacteria," "T1D," "type 1 diabetes," "autoimmune diabetes," "glycemic control," "glucose control," "HbA1c," "inflammation," "inflammatory," and "cytokine." The examination has shown 18,680 articles with relevant keywords. After the exclusion of irrelevant articles, seven observational papers showed a distinct gut microbial signature in T1D patients.

Results

This review shows that, in T1D patients, HbA1c level was negatively correlated with abundance of Prevotella, Faecalibacterium, and Ruminococcaceae and positively correlated with abundance of Dorea formicigenerans, Bacteroidetes, Lactobacillales, and Bacteriodes. Instead, Bifidobacteria was negatively correlated with fasting blood glucose. In addition, there was a positive correlation between Clostridiaceae and time in range. Furthermore, a positive correlation between inflammatory parameters and gut dysbiosis was revealed in T1D patients.

Conclusion

We draw the conclusion that the gut microbiome profiles of T1D patients and healthy controls differ. Patients with T1D may experience leaky gut, bacterial translocation, inflammation, and poor glucose management due to microbiome dysbiosis. Direct manipulation of the gut microbiome in humans and its effects on gut permeability and glycemic control, however, have not been thoroughly investigated. Future research should therefore thoroughly examine other potential pathophysiological mechanisms in larger studies.

Multiple Sclerosis-Associated Gut Microbiome in the Israeli Diverse Populations: Associations with Ethnicity, Gender, Disability Status, Vitamin D Levels, and Mediterranean Diet

Microbiome dysbiosis is increasingly being recognized as implicated in immune-mediated disorders including multiple sclerosis (MS). The microbiome is modulated by genetic and environmental factors including lifestyle, diet, and drug intake. This study aimed to characterize the MS-associated gut microbiome in the Israeli populations and to identify associations with demographic, dietary, and clinical features. The microbiota from 57 treatment-naive patients with MS (PwMS) and 43 age- and gender-matched healthy controls (HCs) was sequenced and abundance compared. Associations between differential microbes with demographic or clinical characteristics, as well as diet and nutrient intake, were assessed. While there was no difference in α- or β-diversity of the microbiome, we identified 40 microbes from different taxonomic levels that differ in abundance between PwMS and HCs, including Barnesiella, Collinsella, Egerthella, Mitsuokella, Olsenella Romboutsia, and Succinivibrio, all enhanced in PwMS, while several members of Lacnospira were reduced. Additional MS-differential microbes specific to ethnicity were identified. Several MS-specific microbial patterns were associated with gender, vitamin D level, Mediterranean diet, nutrient intake, or disability status. Thus, PwMS have altered microbiota composition, with distinctive patterns related to geographic locations and population. Microbiome dysbiosis seem to be implicated in disease progression, gender-related differences, and vitamin D-mediated immunological effects recognized in MS. Dietary interventions may be beneficial in restoring a "healthy microbiota" as part of applying comprehensive personalized therapeutic strategies for PwMS.

From-Toilet-to-Freezer: A Review on Requirements for an Automatic Protocol to Collect and Store Human Fecal Samples for Research Purposes

The composition, viability and metabolic functionality of intestinal microbiota play an important role in human health and disease. Studies on intestinal microbiota are often based on fecal samples, because these can be sampled in a non-invasive way, although procedures for sampling, processing and storage vary. This review presents factors to consider when developing an automated protocol for sampling, processing and storing fecal samples: donor inclusion criteria, urine-feces separation in smart toilets, homogenization, aliquoting, usage or type of buffer to dissolve and store fecal material, temperature and time for processing and storage and quality control. The lack of standardization and low-throughput of state-of-the-art fecal collection procedures promote a more automated protocol. Based on this review, an automated protocol is proposed. Fecal samples should be collected and immediately processed under anaerobic conditions at either room temperature (RT) for a maximum of 4 h or at 4 °C for no more than 24 h. Upon homogenization, preferably in the absence of added solvent to allow addition of a buffer of choice at a later stage, aliquots obtained should be stored at either -20 °C for up to a few months or -80 °C for a longer period-up to 2 years. Protocols for quality control should characterize microbial composition and viability as well as metabolic functionality.

Ambient particulate air pollution and the intestinal microbiome; a systematic review of epidemiological, in vivo and, in vitro studies

A healthy indigenous intestinal microbiome is indispensable for intra- and extra-intestinal human health. Since well-established factors such as diet and antibiotic use only explain 16 % of the inter-individual variation in gut microbiome composition, recent studies have focused on the association between ambient particulate air pollution and the intestinal microbiome. We systematically summarize and discuss all evidence concerning the effect of particulate air pollution on intestinal bacterial diversity indices, specific bacterial taxa, and potential underlying intestinal mechanisms. To this end, all possibly relevant publications published between February 1982 and January 2023 were screened, and eventually, 48 articles were included. The vast majority (n = 35) of these studies were animal studies. The exposure periods investigated in the human epidemiological studies (n = 12) ranged from infancy through elderly. This systematic review found that intestinal microbiome diversity indices were generally negatively associated with particulate air pollution in epidemiological studies, with an increase in taxa belonging to Bacteroidetes (two studies), Deferribacterota (one study), and Proteobacteria (four studies), a decrease in taxa belonging to Verrucomicrobiota (one study), and no consensus for taxa belonging to Actinobacteria (six studies) and Firmicutes (seven studies). There was no unequivocal effect of ambient particulate air pollution exposure on bacterial indices and taxa in animal studies. Only one study in humans examined a possible underlying mechanism; yet, the included in vitro and animal studies depicted higher gut damage, inflammation, oxidative stress, and permeability in exposed versus unexposed animals. Overall, the population-based studies showed a dose-related continuum of short- and long-term ambient particulate air pollution exposure on lower gut diversity and shifts in taxa over the entire life course.