Untargeted approach to investigating the metabolomics profile of type 2 diabetes emiratis

The synergism of Lactobacillaceae, inulin, polyglucose, and aerobic exercise ameliorates hyperglycemia by modulating the gut microbiota community and the metabolic profiles in db/db mice

This study aimed to assess the impact of Lactobacillaceae (L or H represents a low or high dose), inulin (I), and polydextrose (P) combined with aerobic exercise (A) on the composition of the gut microbiota and metabolic profiles in db/db mice. After a 12-week intervention, LIP, LIPA, and HIPA groups exhibited significant improvements in hyperglycemia, glucose tolerance, insulin resistance, inflammatory response, and short-chain fatty acid (SCFA) and blood lipid levels compared to type 2 diabetes mice (MC). After treatment, the gut microbiota composition shifted favorably in the treatment groups which significantly increased the abundance of beneficial bacteria, such as Bacteroides, Blautia, Akkermansia, and Faecalibaculum, and significantly decreased the abundance of Proteus. Metabolomics analysis showed that compared to the MC group, the contents of 5-hydroxyindoleacetic acid, 3-hydroxysebacic acid, adenosine monophosphate (AMP), xanthine and hypoxanthine were significantly decreased, while 3-ketosphinganine, sphinganine, and sphingosine were significantly increased in the LIP and LIPA groups, respectively. Additionally, LIP and LIPA not only improved sphingolipid metabolism and purine metabolism pathways but also activated AMP-activated protein kinase to promote β-oxidation by increasing the levels of SCFAs. Faecalibaculum, Blautia, Bacteroides, and Akkermansia exhibited positive correlations with sphingosine, 3-ketosphinganine, and sphinganine, and exhibited negative correlations with hypoxanthine, xanthine and AMP. Faecalibaculum, Blautia, Bacteroides, and Akkermansia may have the potential to improve sphingolipid metabolism and purine metabolism pathways. These findings suggest that the synergism of Lactobacillaceae, inulin, polydextrose, and aerobic exercise provides a promising strategy for the prevention and management of type 2 diabetes.

The Synergism of Human Lactobacillaceae and Inulin Decrease Hyperglycemia via Regulating the Composition of Gut Microbiota and Metabolic Profiles in db/db Mice

This study aimed to evaluate the effects of Limosilactobacillus fermentum and Lactiplantibacillus plantarum isolated from human feces coordinating with inulin on the composition of gut microbiota and metabolic profiles in db/db mice. These supplements were administered to db/db mice for 12 weeks. The results showed that the Lactobacillaceae coordinating with inulin group (LI) exhibited lower fasting blood glucose levels than the model control group (MC). Additionally, LI was found to enhance colon tissue and increase the levels of short-chain fatty acids. 16S rRNA sequencing revealed that the abundance of Corynebacterium and Proteus, which were significantly increased in the MC group compared with NC group, were significantly decreased by the treatment of LI that also restored the key genera of the Lachnospiraceae_NK4A136_group, Lachnoclostridium, Ruminococcus_gnavus_group, Desulfovibrio, and Lachnospiraceae_UCG-006. Untargeted metabolomics analysis showed that lotaustralin, 5-hydroxyindoleacetic acid, and 13(S)-HpODE were increased while L-phenylalanine and L-tryptophan were decreased in the MC group compared with the NC group. However, the intervention of LI reversed the levels of these metabolites in the intestine. Correlation analysis revealed that Lachnoclostridium and Ruminococcus_gnavus_group were negatively correlated with 5-hydroxyindoleacetic acid and 13(S)-HpODE, but positively correlated with L-tryptophan. 13(S)-HpODE was involved in the "linoleic acid metabolism". L-tryptophan and 5-hydroxyindoleacetic acid were involved in "tryptophan metabolism" and "serotonergic synapse". These findings suggest that LI may alleviate type 2 diabetes symptoms by modulating the abundance of Ruminococcus_gnavus_group and Lachnoclostridium to regulate the pathways of "linoleic acid metabolism", "serotonergic synapse", and" tryptophan metabolism". Our results provide new insights into prevention and treatment of type 2 diabetes.

Integration of systematic review, lipidomics with experiment verification reveals abnormal sphingolipids facilitate diabetic retinopathy by inducing oxidative stress on RMECs

OBJECTIVE

This study aims to explore the potential biomarkers in the development of diabetes mellitus (DM) into diabetic retinopathy (DR).

METHODS

Systematic review of diabetic metabolomics was used to screen the differential metabolites and related pathways during the development of DM. Non-targeted lipidomics of rat plasma was performed to explore the differential metabolites in the development of DM into DR in vivo. To verify the effects of differential metabolites in inducing retinal microvascular endothelial cells (RMECs) injury by increasing oxidative stress, high glucose medium containing differential metabolites was used to induce rat RMECs injury and cell viability, malondialdehyde (MDA) contents, superoxide dismutase (SOD) activities, reactive oxygen species (ROS) levels and mitochondrial membrane potential (MMP) were evaluated in vitro. Network pharmacology was performed to explore the potential mechanism of differential metabolites in inducing DR.

RESULTS

Through the systematic review, 148 differential metabolites were obtained and the sphingolipid metabolic pathway attracted our attention. Plasma non-targeted lipidomics found that sphingolipids were accompanied by the development of DM into DR. In vitro experiments showed sphinganine and sphingosine-1-phosphate aggravated rat RMECs injury induced by high glucose, further increased MDA and ROS levels, and further decreased SOD activities and MMP. Network pharmacology revealed sphinganine and sphingosine-1-phosphate may induce DR by regulating the AGE-RAGE and HIF-1 signaling pathways.

CONCLUSIONS

Integrated systematic review, lipidomics and experiment verification reveal that abnormal sphingolipid metabolism facilitates DR by inducing oxidative stress on RMECs. Our study could provide the experimental basis for finding potential biomarkers for the diagnosis and treatment of DR.

Metabolomics in diabetes mellitus: clinical insight

INTRODUCTION

Diabetes Mellitus (DM) is a chronic heterogeneous metabolic disorder characterized by hyperglycemia due to the destruction of insulin-producing pancreatic β cells and/or insulin resistance. It is now considered a global epidemic disease associated with serious threats to a patient's life. Understanding the metabolic pathways involved in disease pathogenesis and progression is important and would improve prevention and management strategies. Metabolomics is an emerging field of research that offers valuable insights into the metabolic perturbation associated with metabolic diseases, including DM.

AREA COVERED

Herein, we discussed the metabolomics in type 1 and 2 DM research, including its contribution to understanding disease pathogenesis and identifying potential novel biomarkers clinically useful for disease screening, monitoring, and prognosis. In addition, we highlighted the metabolic changes associated with treatment effects, including insulin and different anti-diabetic medications.

EXPERT OPINION

By analyzing the metabolome, the metabolic disturbances involved in T1DM and T2DM can be explored, enhancing our understanding of the disease progression and potentially leading to novel clinical diagnostic and effective new therapeutic approaches. In addition, identifying specific metabolites would be potential clinical biomarkers for predicting the disease and thus preventing and managing hyperglycemia and its complications.

Survey for Computer-Aided Tools and Databases in Metabolomics

Metabolomics has advanced from innovation and functional genomics tools and is currently a basis in the big data-led precision medicine era. Metabolomics is promising in the pharmaceutical field and clinical research. However, due to the complexity and high throughput data generated from such experiments, data mining and analysis are significant challenges for researchers in the field. Therefore, several efforts were made to develop a complete workflow that helps researchers analyze data. This paper introduces a review of the state-of-the-art computer-aided tools and databases in metabolomics established in recent years. The paper provides computational tools and resources based on functionality and accessibility and provides hyperlinks to web pages to download or use. This review aims to present the latest computer-aided tools, databases, and resources to the metabolomics community in one place.