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

Machine Learning and Augmented Intelligence Enables Prognosis of Type 2 Diabetes Prior to Clinical Manifestation

BACKGROUND

The global incidence of type 2 diabetes (T2D) persists at epidemic proportions. Early diagnosis and/or preventive efforts are critical to attenuate the multi-systemic clinical manifestation and consequent healthcare burden. Despite enormous strides in the understanding of pathophysiology and on-going therapeutic development, effectiveness and access are persistent limitations. Among the greatest challenges, the extensive research efforts have not promulgated reliable predictive biomarkers for early detection and risk assessment. The emerging fields of multi-omics combined with machine learning (ML) and augmented intelligence (AI) have profoundly impacted the capacity for predictive, preventive, and personalized medicine.

OBJECTIVE

This paper explores the current challenges associated with the identification of predictive biomarkers for T2D and discusses potential actionable solutions for biomarker identification and validation.

METHODS

The articles included were collected from PubMed queries. The selected topics of inquiry represented a wide range of themes in diabetes biomarker prediction and prognosis.

RESULTS

The current criteria and cutoffs for T2D diagnosis are not optimal nor consider a myriad of contributing factors in terms of early detection. There is an opportunity to leverage AI and ML to significantly enhance the understanding of the underlying mechanisms of the disease and identify prognostic biomarkers. The innovative technologies being developed by GATC are expected to play a crucial role in this pursuit via algorithm training and validation, enabling comprehensive and in-depth analysis of complex biological systems.

CONCLUSION

GATC is an emerging leader guiding the establishment of a systems approach towards research and predictive, personalized medicine. The integration of these technologies with clinical data can contribute to a more comprehensive understanding of T2D, paving the way for precision medicine approaches and improved patient outcomes.

Integration of multi-omics transcriptome-wide analysis for the identification of novel therapeutic drug targets in diabetic retinopathy

BACKGROUND

Diabetic retinopathy (DR) is the most important complication of Type 2 Diabetes (T2D) in eyes. Despite its prevalence, the early detection and management of DR continue to pose considerable challenges. Our research aims to elucidate potent drug targets that could facilitate the identification of DR and propel advancements in its therapeutic strategies.

METHODS

A broad multi-omics exploration of DR was presented to decipher the drug targets of DR and proliferative diabetic retinopathy (PDR). Transcriptome-Wide Association Studies (TWAS), fine-mapping and conditional analysis were applied to unearth potential tissue-specific gene associations with DR. Summary Data-based Mendelian Randomization (SMR) provided secondary analysis of high confidence genes. Cis-instrument of druggable genes were extracted from the eQTLGen Consortium and PsychENCODE, facilitating drug-target MR supported by colocalization analysis. Phenome-Wide Association Studies (PheWAS) was conducted on the high confidence genes. Metabolomic and immunomic MR-profiling further augmented our research as complement.

RESULTS

TWAS identified multiple robust genetic loci in both DR and PDR (WFS1, RPS26, and SRPK1) through genetic associations across different tissues. Meanwhile, we have delineated both the commonalities and discrepancies between DR and PDR at the transcriptomic level, represented by DCLRE1B as the hub gene that DR progressed into PDR. SMR revealed 92 key DR-related genes and 55 PDR-related genes. HLA-DQ family genes have a frequent occurrence, while RPS26, WFS1 and SRPK1 were validated as the genetic network's linchpins. Drug-target MR casted ERBB3 and SRPK1 as candidate effector genes for DR and PDR susceptibility. In addition, metabolomics and immunomics analyses also revealed multifaceted pathogenic factors for DR.

CONCLUSIONS

Our research offers targeted therapeutic insights for early-stage DR and facilitates multi-omic comparisons of it and PDR.

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.